{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimization Methods\n",
    "\n",
    "Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can speed up learning and perhaps even get you to a better final value for the cost function. Having a good optimization algorithm can be the difference between waiting days vs. just a few hours to get a good result. \n",
    "\n",
    "Gradient descent goes \"downhill\" on a cost function $J$. Think of it as trying to do this: \n",
    "<img src=\"images/cost.jpg\" style=\"width:650px;height:300px;\">\n",
    "<caption><center> <u> **Figure 1** </u>: **Minimizing the cost is like finding the lowest point in a hilly landscape**<br> At each step of the training, you update your parameters following a certain direction to try to get to the lowest possible point. </center></caption>\n",
    "\n",
    "**Notations**: As usual, $\\frac{\\partial J}{\\partial a } = $ `da` for any variable `a`.\n",
    "\n",
    "To get started, run the following code to import the libraries you will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "import math\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "\n",
    "from opt_utils import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation\n",
    "from opt_utils import compute_cost, predict, predict_dec, plot_decision_boundary, load_dataset\n",
    "from testCases import *\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Gradient Descent\n",
    "\n",
    "A simple optimization method in machine learning is gradient descent (GD). When you take gradient steps with respect to all $m$ examples on each step, it is also called Batch Gradient Descent. \n",
    "\n",
    "**Warm-up exercise**: Implement the gradient descent update rule. The  gradient descent rule is, for $l = 1, ..., L$: \n",
    "$$ W^{[l]} = W^{[l]} - \\alpha \\text{ } dW^{[l]} \\tag{1}$$\n",
    "$$ b^{[l]} = b^{[l]} - \\alpha \\text{ } db^{[l]} \\tag{2}$$\n",
    "\n",
    "where L is the number of layers and $\\alpha$ is the learning rate. All parameters should be stored in the `parameters` dictionary. Note that the iterator `l` starts at 0 in the `for` loop while the first parameters are $W^{[1]}$ and $b^{[1]}$. You need to shift `l` to `l+1` when coding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters_with_gd\n",
    "\n",
    "def update_parameters_with_gd(parameters, grads, learning_rate):\n",
    "    \"\"\"\n",
    "    Update parameters using one step of gradient descent\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters to be updated:\n",
    "                    parameters['W' + str(l)] = Wl\n",
    "                    parameters['b' + str(l)] = bl\n",
    "    grads -- python dictionary containing your gradients to update each parameters:\n",
    "                    grads['dW' + str(l)] = dWl\n",
    "                    grads['db' + str(l)] = dbl\n",
    "    learning_rate -- the learning rate, scalar.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    \"\"\"\n",
    "\n",
    "    L = len(parameters) // 2 # number of layers in the neural networks\n",
    "\n",
    "    # Update rule for each parameter\n",
    "    for l in range(L):\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        parameters[\"W\" + str(l+1)] = parameters['W'+str(l+1)] - learning_rate * grads['dW'+str(l+1)]\n",
    "        parameters[\"b\" + str(l+1)] = parameters['b'+str(l+1)] - learning_rate * grads['db'+str(l+1)]\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 1.63535156 -0.62320365 -0.53718766]\n",
      " [-1.07799357  0.85639907 -2.29470142]]\n",
      "b1 = [[ 1.74604067]\n",
      " [-0.75184921]]\n",
      "W2 = [[ 0.32171798 -0.25467393  1.46902454]\n",
      " [-2.05617317 -0.31554548 -0.3756023 ]\n",
      " [ 1.1404819  -1.09976462 -0.1612551 ]]\n",
      "b2 = [[-0.88020257]\n",
      " [ 0.02561572]\n",
      " [ 0.57539477]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads, learning_rate = update_parameters_with_gd_test_case()\n",
    "\n",
    "parameters = update_parameters_with_gd(parameters, grads, learning_rate)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td > **W1** </td> \n",
    "           <td > [[ 1.63535156 -0.62320365 -0.53718766]\n",
    " [-1.07799357  0.85639907 -2.29470142]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **b1** </td> \n",
    "           <td > [[ 1.74604067]\n",
    " [-0.75184921]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **W2** </td> \n",
    "           <td > [[ 0.32171798 -0.25467393  1.46902454]\n",
    " [-2.05617317 -0.31554548 -0.3756023 ]\n",
    " [ 1.1404819  -1.09976462 -0.1612551 ]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **b2** </td> \n",
    "           <td > [[-0.88020257]\n",
    " [ 0.02561572]\n",
    " [ 0.57539477]] </td> \n",
    "    </tr> \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A variant of this is Stochastic Gradient Descent (SGD), which is equivalent to mini-batch gradient descent where each mini-batch has just 1 example. The update rule that you have just implemented does not change. What changes is that you would be computing gradients on just one training example at a time, rather than on the whole training set. The code examples below illustrate the difference between stochastic gradient descent and (batch) gradient descent. \n",
    "\n",
    "- **(Batch) Gradient Descent**:\n",
    "\n",
    "``` python\n",
    "X = data_input\n",
    "Y = labels\n",
    "parameters = initialize_parameters(layers_dims)\n",
    "for i in range(0, num_iterations):\n",
    "    # Forward propagation\n",
    "    a, caches = forward_propagation(X, parameters)\n",
    "    # Compute cost.\n",
    "    cost = compute_cost(a, Y)\n",
    "    # Backward propagation.\n",
    "    grads = backward_propagation(a, caches, parameters)\n",
    "    # Update parameters.\n",
    "    parameters = update_parameters(parameters, grads)\n",
    "        \n",
    "```\n",
    "\n",
    "- **Stochastic Gradient Descent**:\n",
    "\n",
    "```python\n",
    "X = data_input\n",
    "Y = labels\n",
    "parameters = initialize_parameters(layers_dims)\n",
    "for i in range(0, num_iterations):\n",
    "    for j in range(0, m):\n",
    "        # Forward propagation\n",
    "        a, caches = forward_propagation(X[:,j], parameters)\n",
    "        # Compute cost\n",
    "        cost = compute_cost(a, Y[:,j])\n",
    "        # Backward propagation\n",
    "        grads = backward_propagation(a, caches, parameters)\n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Stochastic Gradient Descent, you use only 1 training example before updating the gradients. When the training set is large, SGD can be faster. But the parameters will \"oscillate\" toward the minimum rather than converge smoothly. Here is an illustration of this: \n",
    "\n",
    "<img src=\"images/kiank_sgd.png\" style=\"width:750px;height:250px;\">\n",
    "<caption><center> <u> <font color='purple'> **Figure 1** </u><font color='purple'>  : **SGD vs GD**<br> \"+\" denotes a minimum of the cost. SGD leads to many oscillations to reach convergence. But each step is a lot faster to compute for SGD than for GD, as it uses only one training example (vs. the whole batch for GD). </center></caption>\n",
    "\n",
    "**Note** also that implementing SGD requires 3 for-loops in total:\n",
    "1. Over the number of iterations\n",
    "2. Over the $m$ training examples\n",
    "3. Over the layers (to update all parameters, from $(W^{[1]},b^{[1]})$ to $(W^{[L]},b^{[L]})$)\n",
    "\n",
    "In practice, you'll often get faster results if you do not use neither the whole training set, nor only one training example, to perform each update. Mini-batch gradient descent uses an intermediate number of examples for each step. With mini-batch gradient descent, you loop over the mini-batches instead of looping over individual training examples.\n",
    "\n",
    "<img src=\"images/kiank_minibatch.png\" style=\"width:750px;height:250px;\">\n",
    "<caption><center> <u> <font color='purple'> **Figure 2** </u>: <font color='purple'>  **SGD vs Mini-Batch GD**<br> \"+\" denotes a minimum of the cost. Using mini-batches in your optimization algorithm often leads to faster optimization. </center></caption>\n",
    "\n",
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- The difference between gradient descent, mini-batch gradient descent and stochastic gradient descent is the number of examples you use to perform one update step.\n",
    "- You have to tune a learning rate hyperparameter $\\alpha$.\n",
    "- With a well-turned mini-batch size, usually it outperforms either gradient descent or stochastic gradient descent (particularly when the training set is large)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Mini-Batch Gradient descent\n",
    "\n",
    "Let's learn how to build mini-batches from the training set (X, Y).\n",
    "\n",
    "There are two steps:\n",
    "- **Shuffle**: Create a shuffled version of the training set (X, Y) as shown below. Each column of X and Y represents a training example. Note that the random shuffling is done synchronously between X and Y. Such that after the shuffling the $i^{th}$ column of X is the example corresponding to the $i^{th}$ label in Y. The shuffling step ensures that examples will be split randomly into different mini-batches. \n",
    "\n",
    "<img src=\"images/kiank_shuffle.png\" style=\"width:550px;height:300px;\">\n",
    "\n",
    "- **Partition**: Partition the shuffled (X, Y) into mini-batches of size `mini_batch_size` (here 64). Note that the number of training examples is not always divisible by `mini_batch_size`. The last mini batch might be smaller, but you don't need to worry about this. When the final mini-batch is smaller than the full `mini_batch_size`, it will look like this: \n",
    "\n",
    "<img src=\"images/kiank_partition.png\" style=\"width:550px;height:300px;\">\n",
    "\n",
    "**Exercise**: Implement `random_mini_batches`. We coded the shuffling part for you. To help you with the partitioning step, we give you the following code that selects the indexes for the $1^{st}$ and $2^{nd}$ mini-batches:\n",
    "```python\n",
    "first_mini_batch_X = shuffled_X[:, 0 : mini_batch_size]\n",
    "second_mini_batch_X = shuffled_X[:, mini_batch_size : 2 * mini_batch_size]\n",
    "...\n",
    "```\n",
    "\n",
    "Note that the last mini-batch might end up smaller than `mini_batch_size=64`. Let $\\lfloor s \\rfloor$ represents $s$ rounded down to the nearest integer (this is `math.floor(s)` in Python). If the total number of examples is not a multiple of `mini_batch_size=64` then there will be $\\lfloor \\frac{m}{mini\\_batch\\_size}\\rfloor$ mini-batches with a full 64 examples, and the number of examples in the final mini-batch will be ($m-mini_\\_batch_\\_size \\times \\lfloor \\frac{m}{mini\\_batch\\_size}\\rfloor$). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: random_mini_batches\n",
    "\n",
    "def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):\n",
    "    \"\"\"\n",
    "    Creates a list of random minibatches from (X, Y)\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (input size, number of examples)\n",
    "    Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)\n",
    "    mini_batch_size -- size of the mini-batches, integer\n",
    "    \n",
    "    Returns:\n",
    "    mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(seed)            # To make your \"random\" minibatches the same as ours\n",
    "    m = X.shape[1]                  # number of training examples\n",
    "    mini_batches = []\n",
    "        \n",
    "    # Step 1: Shuffle (X, Y)\n",
    "    permutation = list(np.random.permutation(m))\n",
    "    shuffled_X = X[:, permutation]\n",
    "    shuffled_Y = Y[:, permutation].reshape((1,m))\n",
    "\n",
    "    # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.\n",
    "    num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning\n",
    "    for k in range(0, num_complete_minibatches):\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        mini_batch_X = shuffled_X[:, k*mini_batch_size:(k+1)*mini_batch_size]\n",
    "        mini_batch_Y = shuffled_Y[:, k*mini_batch_size:(k+1)*mini_batch_size]\n",
    "        ### END CODE HERE ###\n",
    "        mini_batch = (mini_batch_X, mini_batch_Y)\n",
    "        mini_batches.append(mini_batch)\n",
    "    \n",
    "    # Handling the end case (last mini-batch < mini_batch_size)\n",
    "    if m % mini_batch_size != 0:\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        mini_batch_X = shuffled_X[:, num_complete_minibatches*mini_batch_size:]\n",
    "        mini_batch_Y = shuffled_Y[:, num_complete_minibatches*mini_batch_size:]\n",
    "        ### END CODE HERE ###\n",
    "        mini_batch = (mini_batch_X, mini_batch_Y)\n",
    "        mini_batches.append(mini_batch)\n",
    "    \n",
    "    return mini_batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of the 1st mini_batch_X: (12288, 64)\n",
      "shape of the 2nd mini_batch_X: (12288, 64)\n",
      "shape of the 3rd mini_batch_X: (12288, 20)\n",
      "shape of the 1st mini_batch_Y: (1, 64)\n",
      "shape of the 2nd mini_batch_Y: (1, 64)\n",
      "shape of the 3rd mini_batch_Y: (1, 20)\n",
      "mini batch sanity check: [ 0.90085595 -0.7612069   0.2344157 ]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess, mini_batch_size = random_mini_batches_test_case()\n",
    "mini_batches = random_mini_batches(X_assess, Y_assess, mini_batch_size)\n",
    "\n",
    "print (\"shape of the 1st mini_batch_X: \" + str(mini_batches[0][0].shape))\n",
    "print (\"shape of the 2nd mini_batch_X: \" + str(mini_batches[1][0].shape))\n",
    "print (\"shape of the 3rd mini_batch_X: \" + str(mini_batches[2][0].shape))\n",
    "print (\"shape of the 1st mini_batch_Y: \" + str(mini_batches[0][1].shape))\n",
    "print (\"shape of the 2nd mini_batch_Y: \" + str(mini_batches[1][1].shape)) \n",
    "print (\"shape of the 3rd mini_batch_Y: \" + str(mini_batches[2][1].shape))\n",
    "print (\"mini batch sanity check: \" + str(mini_batches[0][0][0][0:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:50%\"> \n",
    "    <tr>\n",
    "    <td > **shape of the 1st mini_batch_X** </td> \n",
    "           <td > (12288, 64) </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **shape of the 2nd mini_batch_X** </td> \n",
    "           <td > (12288, 64) </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **shape of the 3rd mini_batch_X** </td> \n",
    "           <td > (12288, 20) </td> \n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td > **shape of the 1st mini_batch_Y** </td> \n",
    "           <td > (1, 64) </td> \n",
    "    </tr> \n",
    "    <tr>\n",
    "    <td > **shape of the 2nd mini_batch_Y** </td> \n",
    "           <td > (1, 64) </td> \n",
    "    </tr> \n",
    "    <tr>\n",
    "    <td > **shape of the 3rd mini_batch_Y** </td> \n",
    "           <td > (1, 20) </td> \n",
    "    </tr> \n",
    "    <tr>\n",
    "    <td > **mini batch sanity check** </td> \n",
    "           <td > [ 0.90085595 -0.7612069   0.2344157 ] </td> \n",
    "    </tr>\n",
    "    \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- Shuffling and Partitioning are the two steps required to build mini-batches\n",
    "- Powers of two are often chosen to be the mini-batch size, e.g., 16, 32, 64, 128."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Momentum\n",
    "\n",
    "Because mini-batch gradient descent makes a parameter update after seeing just a subset of examples, the direction of the update has some variance, and so the path taken by mini-batch gradient descent will \"oscillate\" toward convergence. Using momentum can reduce these oscillations. \n",
    "\n",
    "Momentum takes into account the past gradients to smooth out the update. We will store the 'direction' of the previous gradients in the variable $v$. Formally, this will be the exponentially weighted average of the gradient on previous steps. You can also think of $v$ as the \"velocity\" of a ball rolling downhill, building up speed (and momentum) according to the direction of the gradient/slope of the hill. \n",
    "\n",
    "<img src=\"images/opt_momentum.png\" style=\"width:400px;height:250px;\">\n",
    "<caption><center> <u><font color='purple'>**Figure 3**</u><font color='purple'>: The red arrows shows the direction taken by one step of mini-batch gradient descent with momentum. The blue points show the direction of the gradient (with respect to the current mini-batch) on each step. Rather than just following the gradient, we let the gradient influence $v$ and then take a step in the direction of $v$.<br> <font color='black'> </center>\n",
    "\n",
    "\n",
    "**Exercise**: Initialize the velocity. The velocity, $v$, is a python dictionary that needs to be initialized with arrays of zeros. Its keys are the same as those in the `grads` dictionary, that is:\n",
    "for $l =1,...,L$:\n",
    "```python\n",
    "v[\"dW\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"W\" + str(l+1)])\n",
    "v[\"db\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"b\" + str(l+1)])\n",
    "```\n",
    "**Note** that the iterator l starts at 0 in the for loop while the first parameters are v[\"dW1\"] and v[\"db1\"] (that's a \"one\" on the superscript). This is why we are shifting l to l+1 in the `for` loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_velocity\n",
    "\n",
    "def initialize_velocity(parameters):\n",
    "    \"\"\"\n",
    "    Initializes the velocity as a python dictionary with:\n",
    "                - keys: \"dW1\", \"db1\", ..., \"dWL\", \"dbL\" \n",
    "                - values: numpy arrays of zeros of the same shape as the corresponding gradients/parameters.\n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters.\n",
    "                    parameters['W' + str(l)] = Wl\n",
    "                    parameters['b' + str(l)] = bl\n",
    "    \n",
    "    Returns:\n",
    "    v -- python dictionary containing the current velocity.\n",
    "                    v['dW' + str(l)] = velocity of dWl\n",
    "                    v['db' + str(l)] = velocity of dbl\n",
    "    \"\"\"\n",
    "    \n",
    "    L = len(parameters) // 2 # number of layers in the neural networks\n",
    "    v = {}\n",
    "    \n",
    "    # Initialize velocity\n",
    "    for l in range(L):\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        v[\"dW\" + str(l+1)] = np.zeros_like(parameters['W'+str(l+1)])\n",
    "        v[\"db\" + str(l+1)] = np.zeros_like(parameters['b'+str(l+1)])\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "    return v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v[\"dW1\"] = [[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n",
      "v[\"db1\"] = [[ 0.]\n",
      " [ 0.]]\n",
      "v[\"dW2\"] = [[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n",
      "v[\"db2\"] = [[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_velocity_test_case()\n",
    "\n",
    "v = initialize_velocity(parameters)\n",
    "print(\"v[\\\"dW1\\\"] = \" + str(v[\"dW1\"]))\n",
    "print(\"v[\\\"db1\\\"] = \" + str(v[\"db1\"]))\n",
    "print(\"v[\\\"dW2\\\"] = \" + str(v[\"dW2\"]))\n",
    "print(\"v[\\\"db2\\\"] = \" + str(v[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:40%\"> \n",
    "    <tr>\n",
    "    <td > **v[\"dW1\"]** </td> \n",
    "           <td > [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"db1\"]** </td> \n",
    "           <td > [[ 0.]\n",
    " [ 0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"dW2\"]** </td> \n",
    "           <td > [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]\n",
    " [ 0.  0.  0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"db2\"]** </td> \n",
    "           <td > [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "    </tr> \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**:  Now, implement the parameters update with momentum. The momentum update rule is, for $l = 1, ..., L$: \n",
    "\n",
    "$$ \\begin{cases}\n",
    "v_{dW^{[l]}} = \\beta v_{dW^{[l]}} + (1 - \\beta) dW^{[l]} \\\\\n",
    "W^{[l]} = W^{[l]} - \\alpha v_{dW^{[l]}}\n",
    "\\end{cases}\\tag{3}$$\n",
    "\n",
    "$$\\begin{cases}\n",
    "v_{db^{[l]}} = \\beta v_{db^{[l]}} + (1 - \\beta) db^{[l]} \\\\\n",
    "b^{[l]} = b^{[l]} - \\alpha v_{db^{[l]}} \n",
    "\\end{cases}\\tag{4}$$\n",
    "\n",
    "where L is the number of layers, $\\beta$ is the momentum and $\\alpha$ is the learning rate. All parameters should be stored in the `parameters` dictionary.  Note that the iterator `l` starts at 0 in the `for` loop while the first parameters are $W^{[1]}$ and $b^{[1]}$ (that's a \"one\" on the superscript). So you will need to shift `l` to `l+1` when coding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters_with_momentum\n",
    "\n",
    "def update_parameters_with_momentum(parameters, grads, v, beta, learning_rate):\n",
    "    \"\"\"\n",
    "    Update parameters using Momentum\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters:\n",
    "                    parameters['W' + str(l)] = Wl\n",
    "                    parameters['b' + str(l)] = bl\n",
    "    grads -- python dictionary containing your gradients for each parameters:\n",
    "                    grads['dW' + str(l)] = dWl\n",
    "                    grads['db' + str(l)] = dbl\n",
    "    v -- python dictionary containing the current velocity:\n",
    "                    v['dW' + str(l)] = ...\n",
    "                    v['db' + str(l)] = ...\n",
    "    beta -- the momentum hyperparameter, scalar\n",
    "    learning_rate -- the learning rate, scalar\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    v -- python dictionary containing your updated velocities\n",
    "    \"\"\"\n",
    "\n",
    "    L = len(parameters) // 2 # number of layers in the neural networks\n",
    "    \n",
    "    # Momentum update for each parameter\n",
    "    for l in range(L):\n",
    "        \n",
    "        ### START CODE HERE ### (approx. 4 lines)\n",
    "        # compute velocities\n",
    "        v[\"dW\" + str(l+1)] = beta * v[\"dW\" + str(l+1)] + (1 - beta) * grads['dW'+str(l+1)]\n",
    "        v[\"db\" + str(l+1)] = beta * v[\"db\" + str(l+1)] + (1 - beta) * grads['db'+str(l+1)]\n",
    "        # update parameters\n",
    "        parameters[\"W\" + str(l+1)] = parameters[\"W\" + str(l+1)] - learning_rate * v[\"dW\" + str(l+1)]\n",
    "        parameters[\"b\" + str(l+1)] = parameters[\"b\" + str(l+1)] - learning_rate * v[\"db\" + str(l+1)]\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "    return parameters, v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 1.62544598 -0.61290114 -0.52907334]\n",
      " [-1.07347112  0.86450677 -2.30085497]]\n",
      "b1 = [[ 1.74493465]\n",
      " [-0.76027113]]\n",
      "W2 = [[ 0.31930698 -0.24990073  1.4627996 ]\n",
      " [-2.05974396 -0.32173003 -0.38320915]\n",
      " [ 1.13444069 -1.0998786  -0.1713109 ]]\n",
      "b2 = [[-0.87809283]\n",
      " [ 0.04055394]\n",
      " [ 0.58207317]]\n",
      "v[\"dW1\"] = [[-0.11006192  0.11447237  0.09015907]\n",
      " [ 0.05024943  0.09008559 -0.06837279]]\n",
      "v[\"db1\"] = [[-0.01228902]\n",
      " [-0.09357694]]\n",
      "v[\"dW2\"] = [[-0.02678881  0.05303555 -0.06916608]\n",
      " [-0.03967535 -0.06871727 -0.08452056]\n",
      " [-0.06712461 -0.00126646 -0.11173103]]\n",
      "v[\"db2\"] = [[ 0.02344157]\n",
      " [ 0.16598022]\n",
      " [ 0.07420442]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads, v = update_parameters_with_momentum_test_case()\n",
    "\n",
    "parameters, v = update_parameters_with_momentum(parameters, grads, v, beta = 0.9, learning_rate = 0.01)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))\n",
    "print(\"v[\\\"dW1\\\"] = \" + str(v[\"dW1\"]))\n",
    "print(\"v[\\\"db1\\\"] = \" + str(v[\"db1\"]))\n",
    "print(\"v[\\\"dW2\\\"] = \" + str(v[\"dW2\"]))\n",
    "print(\"v[\\\"db2\\\"] = \" + str(v[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\"> \n",
    "    <tr>\n",
    "    <td > **W1** </td> \n",
    "           <td > [[ 1.62544598 -0.61290114 -0.52907334]\n",
    " [-1.07347112  0.86450677 -2.30085497]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **b1** </td> \n",
    "           <td > [[ 1.74493465]\n",
    " [-0.76027113]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **W2** </td> \n",
    "           <td > [[ 0.31930698 -0.24990073  1.4627996 ]\n",
    " [-2.05974396 -0.32173003 -0.38320915]\n",
    " [ 1.13444069 -1.0998786  -0.1713109 ]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **b2** </td> \n",
    "           <td > [[-0.87809283]\n",
    " [ 0.04055394]\n",
    " [ 0.58207317]] </td> \n",
    "    </tr> \n",
    "\n",
    "    <tr>\n",
    "    <td > **v[\"dW1\"]** </td> \n",
    "           <td > [[-0.11006192  0.11447237  0.09015907]\n",
    " [ 0.05024943  0.09008559 -0.06837279]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"db1\"]** </td> \n",
    "           <td > [[-0.01228902]\n",
    " [-0.09357694]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"dW2\"]** </td> \n",
    "           <td > [[-0.02678881  0.05303555 -0.06916608]\n",
    " [-0.03967535 -0.06871727 -0.08452056]\n",
    " [-0.06712461 -0.00126646 -0.11173103]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"db2\"]** </td> \n",
    "           <td > [[ 0.02344157]\n",
    " [ 0.16598022]\n",
    " [ 0.07420442]]</td> \n",
    "    </tr> \n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Note** that:\n",
    "- The velocity is initialized with zeros. So the algorithm will take a few iterations to \"build up\" velocity and start to take bigger steps.\n",
    "- If $\\beta = 0$, then this just becomes standard gradient descent without momentum. \n",
    "\n",
    "**How do you choose $\\beta$?**\n",
    "\n",
    "- The larger the momentum $\\beta$ is, the smoother the update because the more we take the past gradients into account. But if $\\beta$ is too big, it could also smooth out the updates too much. \n",
    "- Common values for $\\beta$ range from 0.8 to 0.999. If you don't feel inclined to tune this, $\\beta = 0.9$ is often a reasonable default. \n",
    "- Tuning the optimal $\\beta$ for your model might need trying several values to see what works best in term of reducing the value of the cost function $J$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- Momentum takes past gradients into account to smooth out the steps of gradient descent. It can be applied with batch gradient descent, mini-batch gradient descent or stochastic gradient descent.\n",
    "- You have to tune a momentum hyperparameter $\\beta$ and a learning rate $\\alpha$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Adam\n",
    "\n",
    "Adam is one of the most effective optimization algorithms for training neural networks. It combines ideas from RMSProp (described in lecture) and Momentum. \n",
    "\n",
    "**How does Adam work?**\n",
    "1. It calculates an exponentially weighted average of past gradients, and stores it in variables $v$ (before bias correction) and $v^{corrected}$ (with bias correction). \n",
    "2. It calculates an exponentially weighted average of the squares of the past gradients, and  stores it in variables $s$ (before bias correction) and $s^{corrected}$ (with bias correction). \n",
    "3. It updates parameters in a direction based on combining information from \"1\" and \"2\".\n",
    "\n",
    "The update rule is, for $l = 1, ..., L$: \n",
    "\n",
    "$$\\begin{cases}\n",
    "v_{dW^{[l]}} = \\beta_1 v_{dW^{[l]}} + (1 - \\beta_1) \\frac{\\partial \\mathcal{J} }{ \\partial W^{[l]} } \\\\\n",
    "v^{corrected}_{dW^{[l]}} = \\frac{v_{dW^{[l]}}}{1 - (\\beta_1)^t} \\\\\n",
    "s_{dW^{[l]}} = \\beta_2 s_{dW^{[l]}} + (1 - \\beta_2) (\\frac{\\partial \\mathcal{J} }{\\partial W^{[l]} })^2 \\\\\n",
    "s^{corrected}_{dW^{[l]}} = \\frac{s_{dW^{[l]}}}{1 - (\\beta_1)^t} \\\\\n",
    "W^{[l]} = W^{[l]} - \\alpha \\frac{v^{corrected}_{dW^{[l]}}}{\\sqrt{s^{corrected}_{dW^{[l]}}} + \\varepsilon}\n",
    "\\end{cases}$$\n",
    "where:\n",
    "- t counts the number of steps taken of Adam \n",
    "- L is the number of layers\n",
    "- $\\beta_1$ and $\\beta_2$ are hyperparameters that control the two exponentially weighted averages. \n",
    "- $\\alpha$ is the learning rate\n",
    "- $\\varepsilon$ is a very small number to avoid dividing by zero\n",
    "\n",
    "As usual, we will store all parameters in the `parameters` dictionary  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Initialize the Adam variables $v, s$ which keep track of the past information.\n",
    "\n",
    "**Instruction**: The variables $v, s$ are python dictionaries that need to be initialized with arrays of zeros. Their keys are the same as for `grads`, that is:\n",
    "for $l = 1, ..., L$:\n",
    "```python\n",
    "v[\"dW\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"W\" + str(l+1)])\n",
    "v[\"db\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"b\" + str(l+1)])\n",
    "s[\"dW\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"W\" + str(l+1)])\n",
    "s[\"db\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"b\" + str(l+1)])\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_adam\n",
    "\n",
    "def initialize_adam(parameters) :\n",
    "    \"\"\"\n",
    "    Initializes v and s as two python dictionaries with:\n",
    "                - keys: \"dW1\", \"db1\", ..., \"dWL\", \"dbL\" \n",
    "                - values: numpy arrays of zeros of the same shape as the corresponding gradients/parameters.\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters.\n",
    "                    parameters[\"W\" + str(l)] = Wl\n",
    "                    parameters[\"b\" + str(l)] = bl\n",
    "    \n",
    "    Returns: \n",
    "    v -- python dictionary that will contain the exponentially weighted average of the gradient.\n",
    "                    v[\"dW\" + str(l)] = ...\n",
    "                    v[\"db\" + str(l)] = ...\n",
    "    s -- python dictionary that will contain the exponentially weighted average of the squared gradient.\n",
    "                    s[\"dW\" + str(l)] = ...\n",
    "                    s[\"db\" + str(l)] = ...\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    L = len(parameters) // 2 # number of layers in the neural networks\n",
    "    v = {}\n",
    "    s = {}\n",
    "    \n",
    "    # Initialize v, s. Input: \"parameters\". Outputs: \"v, s\".\n",
    "    for l in range(L):\n",
    "    ### START CODE HERE ### (approx. 4 lines)\n",
    "        v[\"dW\" + str(l+1)] = np.zeros_like(parameters['W'+str(l+1)])\n",
    "        v[\"db\" + str(l+1)] = np.zeros_like(parameters['b'+str(l+1)])\n",
    "        s[\"dW\" + str(l+1)] = np.zeros_like(parameters['W'+str(l+1)])\n",
    "        s[\"db\" + str(l+1)] = np.zeros_like(parameters['b'+str(l+1)])\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return v, s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v[\"dW1\"] = [[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n",
      "v[\"db1\"] = [[ 0.]\n",
      " [ 0.]]\n",
      "v[\"dW2\"] = [[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n",
      "v[\"db2\"] = [[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n",
      "s[\"dW1\"] = [[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n",
      "s[\"db1\"] = [[ 0.]\n",
      " [ 0.]]\n",
      "s[\"dW2\"] = [[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n",
      "s[\"db2\"] = [[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_adam_test_case()\n",
    "\n",
    "v, s = initialize_adam(parameters)\n",
    "print(\"v[\\\"dW1\\\"] = \" + str(v[\"dW1\"]))\n",
    "print(\"v[\\\"db1\\\"] = \" + str(v[\"db1\"]))\n",
    "print(\"v[\\\"dW2\\\"] = \" + str(v[\"dW2\"]))\n",
    "print(\"v[\\\"db2\\\"] = \" + str(v[\"db2\"]))\n",
    "print(\"s[\\\"dW1\\\"] = \" + str(s[\"dW1\"]))\n",
    "print(\"s[\\\"db1\\\"] = \" + str(s[\"db1\"]))\n",
    "print(\"s[\\\"dW2\\\"] = \" + str(s[\"dW2\"]))\n",
    "print(\"s[\\\"db2\\\"] = \" + str(s[\"db2\"]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:40%\"> \n",
    "    <tr>\n",
    "    <td > **v[\"dW1\"]** </td> \n",
    "           <td > [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"db1\"]** </td> \n",
    "           <td > [[ 0.]\n",
    " [ 0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"dW2\"]** </td> \n",
    "           <td > [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]\n",
    " [ 0.  0.  0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"db2\"]** </td> \n",
    "           <td > [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "    </tr> \n",
    "    <tr>\n",
    "    <td > **s[\"dW1\"]** </td> \n",
    "           <td > [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **s[\"db1\"]** </td> \n",
    "           <td > [[ 0.]\n",
    " [ 0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **s[\"dW2\"]** </td> \n",
    "           <td > [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]\n",
    " [ 0.  0.  0.]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **s[\"db2\"]** </td> \n",
    "           <td > [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "    </tr>\n",
    "\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**:  Now, implement the parameters update with Adam. Recall the general update rule is, for $l = 1, ..., L$: \n",
    "\n",
    "$$\\begin{cases}\n",
    "v_{W^{[l]}} = \\beta_1 v_{W^{[l]}} + (1 - \\beta_1) \\frac{\\partial J }{ \\partial W^{[l]} } \\\\\n",
    "v^{corrected}_{W^{[l]}} = \\frac{v_{W^{[l]}}}{1 - (\\beta_1)^t} \\\\\n",
    "s_{W^{[l]}} = \\beta_2 s_{W^{[l]}} + (1 - \\beta_2) (\\frac{\\partial J }{\\partial W^{[l]} })^2 \\\\\n",
    "s^{corrected}_{W^{[l]}} = \\frac{s_{W^{[l]}}}{1 - (\\beta_2)^t} \\\\\n",
    "W^{[l]} = W^{[l]} - \\alpha \\frac{v^{corrected}_{W^{[l]}}}{\\sqrt{s^{corrected}_{W^{[l]}}}+\\varepsilon}\n",
    "\\end{cases}$$\n",
    "\n",
    "\n",
    "**Note** that the iterator `l` starts at 0 in the `for` loop while the first parameters are $W^{[1]}$ and $b^{[1]}$. You need to shift `l` to `l+1` when coding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters_with_adam\n",
    "\n",
    "def update_parameters_with_adam(parameters, grads, v, s, t, learning_rate = 0.01,\n",
    "                                beta1 = 0.9, beta2 = 0.999,  epsilon = 1e-8):\n",
    "    \"\"\"\n",
    "    Update parameters using Adam\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters:\n",
    "                    parameters['W' + str(l)] = Wl\n",
    "                    parameters['b' + str(l)] = bl\n",
    "    grads -- python dictionary containing your gradients for each parameters:\n",
    "                    grads['dW' + str(l)] = dWl\n",
    "                    grads['db' + str(l)] = dbl\n",
    "    v -- Adam variable, moving average of the first gradient, python dictionary\n",
    "    s -- Adam variable, moving average of the squared gradient, python dictionary\n",
    "    learning_rate -- the learning rate, scalar.\n",
    "    beta1 -- Exponential decay hyperparameter for the first moment estimates \n",
    "    beta2 -- Exponential decay hyperparameter for the second moment estimates \n",
    "    epsilon -- hyperparameter preventing division by zero in Adam updates\n",
    "\n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    v -- Adam variable, moving average of the first gradient, python dictionary\n",
    "    s -- Adam variable, moving average of the squared gradient, python dictionary\n",
    "    \"\"\"\n",
    "    \n",
    "    L = len(parameters) // 2                 # number of layers in the neural networks\n",
    "    v_corrected = {}                         # Initializing first moment estimate, python dictionary\n",
    "    s_corrected = {}                         # Initializing second moment estimate, python dictionary\n",
    "    \n",
    "    # Perform Adam update on all parameters\n",
    "    for l in range(L):\n",
    "        # Moving average of the gradients. Inputs: \"v, grads, beta1\". Output: \"v\".\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        v[\"dW\" + str(l+1)] = beta1 * v[\"dW\" + str(l+1)] + (1 - beta1) * grads['dW'+str(l+1)]\n",
    "        v[\"db\" + str(l+1)] = beta1 * v[\"db\" + str(l+1)] + (1 - beta1) * grads['db'+str(l+1)]\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        # Compute bias-corrected first moment estimate. Inputs: \"v, beta1, t\". Output: \"v_corrected\".\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        v_corrected[\"dW\" + str(l+1)] = v[\"dW\" + str(l+1)] / (1 - beta1**t)\n",
    "        v_corrected[\"db\" + str(l+1)] = v[\"db\" + str(l+1)] / (1 - beta1**t)\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        # Moving average of the squared gradients. Inputs: \"s, grads, beta2\". Output: \"s\".\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        s[\"dW\" + str(l+1)] = beta2 * s[\"dW\" + str(l+1)] + (1 - beta2) * (grads[\"dW\" + str(l+1)])**2\n",
    "        s[\"db\" + str(l+1)] = beta2 * s[\"db\" + str(l+1)] + (1 - beta2) * (grads[\"db\" + str(l+1)])**2\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        # Compute bias-corrected second raw moment estimate. Inputs: \"s, beta2, t\". Output: \"s_corrected\".\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        s_corrected[\"dW\" + str(l+1)] = s[\"dW\" + str(l+1)] / (1 - beta2**t)\n",
    "        s_corrected[\"db\" + str(l+1)] = s[\"db\" + str(l+1)] / (1 - beta2**t)\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        # Update parameters. Inputs: \"parameters, learning_rate, v_corrected, s_corrected, epsilon\". Output: \"parameters\".\n",
    "        ### START CODE HERE ### (approx. 2 lines)\n",
    "        parameters[\"W\" + str(l+1)] = parameters[\"W\" + str(l+1)] - learning_rate * v_corrected[\"dW\" + str(l+1)] / (np.sqrt(s_corrected[\"dW\" + str(l+1)]) + epsilon)\n",
    "        parameters[\"b\" + str(l+1)] = parameters[\"b\" + str(l+1)] - learning_rate * v_corrected[\"db\" + str(l+1)] / (np.sqrt(s_corrected[\"db\" + str(l+1)]) + epsilon)\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "    return parameters, v, s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 1.63178673 -0.61919778 -0.53561312]\n",
      " [-1.08040999  0.85796626 -2.29409733]]\n",
      "b1 = [[ 1.75225313]\n",
      " [-0.75376553]]\n",
      "W2 = [[ 0.32648046 -0.25681174  1.46954931]\n",
      " [-2.05269934 -0.31497584 -0.37661299]\n",
      " [ 1.14121081 -1.09244991 -0.16498684]]\n",
      "b2 = [[-0.88529979]\n",
      " [ 0.03477238]\n",
      " [ 0.57537385]]\n",
      "v[\"dW1\"] = [[-0.11006192  0.11447237  0.09015907]\n",
      " [ 0.05024943  0.09008559 -0.06837279]]\n",
      "v[\"db1\"] = [[-0.01228902]\n",
      " [-0.09357694]]\n",
      "v[\"dW2\"] = [[-0.02678881  0.05303555 -0.06916608]\n",
      " [-0.03967535 -0.06871727 -0.08452056]\n",
      " [-0.06712461 -0.00126646 -0.11173103]]\n",
      "v[\"db2\"] = [[ 0.02344157]\n",
      " [ 0.16598022]\n",
      " [ 0.07420442]]\n",
      "s[\"dW1\"] = [[ 0.00121136  0.00131039  0.00081287]\n",
      " [ 0.0002525   0.00081154  0.00046748]]\n",
      "s[\"db1\"] = [[  1.51020075e-05]\n",
      " [  8.75664434e-04]]\n",
      "s[\"dW2\"] = [[  7.17640232e-05   2.81276921e-04   4.78394595e-04]\n",
      " [  1.57413361e-04   4.72206320e-04   7.14372576e-04]\n",
      " [  4.50571368e-04   1.60392066e-07   1.24838242e-03]]\n",
      "s[\"db2\"] = [[  5.49507194e-05]\n",
      " [  2.75494327e-03]\n",
      " [  5.50629536e-04]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads, v, s = update_parameters_with_adam_test_case()\n",
    "parameters, v, s  = update_parameters_with_adam(parameters, grads, v, s, t = 2)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))\n",
    "print(\"v[\\\"dW1\\\"] = \" + str(v[\"dW1\"]))\n",
    "print(\"v[\\\"db1\\\"] = \" + str(v[\"db1\"]))\n",
    "print(\"v[\\\"dW2\\\"] = \" + str(v[\"dW2\"]))\n",
    "print(\"v[\\\"db2\\\"] = \" + str(v[\"db2\"]))\n",
    "print(\"s[\\\"dW1\\\"] = \" + str(s[\"dW1\"]))\n",
    "print(\"s[\\\"db1\\\"] = \" + str(s[\"db1\"]))\n",
    "print(\"s[\\\"dW2\\\"] = \" + str(s[\"dW2\"]))\n",
    "print(\"s[\\\"db2\\\"] = \" + str(s[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td > **W1** </td> \n",
    "           <td > [[ 1.63178673 -0.61919778 -0.53561312]\n",
    " [-1.08040999  0.85796626 -2.29409733]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **b1** </td> \n",
    "           <td > [[ 1.75225313]\n",
    " [-0.75376553]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **W2** </td> \n",
    "           <td > [[ 0.32648046 -0.25681174  1.46954931]\n",
    " [-2.05269934 -0.31497584 -0.37661299]\n",
    " [ 1.14121081 -1.09245036 -0.16498684]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **b2** </td> \n",
    "           <td > [[-0.88529978]\n",
    " [ 0.03477238]\n",
    " [ 0.57537385]] </td> \n",
    "    </tr> \n",
    "    <tr>\n",
    "    <td > **v[\"dW1\"]** </td> \n",
    "           <td > [[-0.11006192  0.11447237  0.09015907]\n",
    " [ 0.05024943  0.09008559 -0.06837279]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"db1\"]** </td> \n",
    "           <td > [[-0.01228902]\n",
    " [-0.09357694]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"dW2\"]** </td> \n",
    "           <td > [[-0.02678881  0.05303555 -0.06916608]\n",
    " [-0.03967535 -0.06871727 -0.08452056]\n",
    " [-0.06712461 -0.00126646 -0.11173103]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **v[\"db2\"]** </td> \n",
    "           <td > [[ 0.02344157]\n",
    " [ 0.16598022]\n",
    " [ 0.07420442]] </td> \n",
    "    </tr> \n",
    "    <tr>\n",
    "    <td > **s[\"dW1\"]** </td> \n",
    "           <td > [[ 0.00121136  0.00131039  0.00081287]\n",
    " [ 0.0002525   0.00081154  0.00046748]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **s[\"db1\"]** </td> \n",
    "           <td > [[  1.51020075e-05]\n",
    " [  8.75664434e-04]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **s[\"dW2\"]** </td> \n",
    "           <td > [[  7.17640232e-05   2.81276921e-04   4.78394595e-04]\n",
    " [  1.57413361e-04   4.72206320e-04   7.14372576e-04]\n",
    " [  4.50571368e-04   1.60392066e-07   1.24838242e-03]] </td> \n",
    "    </tr> \n",
    "    \n",
    "    <tr>\n",
    "    <td > **s[\"db2\"]** </td> \n",
    "           <td > [[  5.49507194e-05]\n",
    " [  2.75494327e-03]\n",
    " [  5.50629536e-04]] </td> \n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now have three working optimization algorithms (mini-batch gradient descent, Momentum, Adam). Let's implement a model with each of these optimizers and observe the difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Model with different optimization algorithms\n",
    "\n",
    "Lets use the following \"moons\" dataset to test the different optimization methods. (The dataset is named \"moons\" because the data from each of the two classes looks a bit like a crescent-shaped moon.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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EdAph2LBhjg4z04IFCzhwKJJXLdWoofww3BhK9VEu9KcSNZUfY//zJjab7Q5ncryUlBQA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\no1zz2vj6+vL555/r3HfdunU4KsyojrXW8XaUJSkluUhytF4GY2Njlq9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/Tx48fs2bOH+Ph4HBwc6Nq1a74n7efnN37Bk7MJ2qSmpeb5XVJSEr7+fgzBrUC1+JpqC9b6+pCc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7l44liiINGzRi/W/r8PLywtPTkzFjxrBs2XJApFqlrihV+Rw8OZMiZT5yuS5ujg1xsKnBuHGf\n0KtXL61CtGVRUFCAhWHZwr26CsPSfv8VCgoK0TEp+3dPV2HwuN9/Z81vElKV5VvIw4cPmfz5F7TB\niVFCNdwEU/QEOe6CKaPwpjWOfD7p879dXSQh8ao5duwY33//PXWrDaBdoyl4urbA3bEhTeqMol3j\nKVw4f5HBgwdz8uRJlEpNf7/yoqOjw/Dhw7kUcpHc3BymTJmMXKZD83ofIZOV5D15urZAodAn4NxP\nZOemlt6rKlYSGulHdNxppn45BYVCwZkzZzDQN8augrfW5xkbVsDGyoPTp0+rXR86bChxSVeJTTiH\nj2dXtWAMSgKNWlV6YWJkxfLly8tcj7d3FdIyIjXsiZ6QnBYOQJUqVZ77bubP/xFbK0/qePcrDcYA\nZDIFdbz7YWJsQ1JqOPcfaHqBqoqVhEcfompVb6Kjo4mNjX3u856msLCQ1NRUFI+Dpb8iCDK8PTry\n6NEjTp08R+PaH/Bel18Y2HUNLet/wq2IezRt0ozp06djb+/AsmXLkMlkKOR6RN09wd3EC1Rxb0ez\numOp7tmNpNRQ7j+4RVpaKvv2/SlYLIoiMTExREREkJubqzEPgOrVq5Py4GaZa0lMvYFCoYOXl9cL\nvYM3mZo1a5CSFlZme+L9G+jo6Go1d5f4+0gB2VvIpk2bUBYV0Q03jTZBEOiGG0WFhWzZsuWfn5yE\nxDNYumQpVhaueHt01GiztvTE07UVe/bspUWLFri6uLFy5cpXUmp/6tQp7K2roaf7pweivp4pbRt9\nTm7eA/YcncTh03MIuriUPccmcPnmdqZMmcJHH30EUL45iJr9Bg0ahJOjI8WiChd77cdaMpkcB5va\npblc2vjwww95lJVC1N0TGm0qVSE3Iv+gevWaz60mzM3NJSDgOO5OTbQWEQiCQBX39oBA4IWfuBVz\nnKKiPERR5P6D2xwPns/9h9GEh9+kW7duVKxYkY4dOxEZGfnM56pUKubMmYOTozMbNmxAqSrkj+OT\nCTi/kMzsFLW+SalhCIKM9o2/pJJLMxQKPWQyBS4OeGj5uQAAIABJREFU9WjX6Etyc4v47rvvsLes\nR8+2C3C0qY0gCBgaWtK99Q/U9n4Xd6eG1PDqTvfWc7GyqIhMkHPr1i1EUeTnn3/Gy6sqFStWpGrV\nqtjY2DJu3DjS0tLU5jF27BhS0m5zL/GS5nvMz+BWzGF69eqJtbX1M9f+NjF27BiS025pX3NeOhGx\nh+nTp88zvWolXp7XdmQpCIIFsAzoChQDu4DxoijmPOOedcD7f7nsL4pi59c1z7eRyMhIHBQmmCq1\nb7+bCXrYK0y4fVvzG66ExL/JqdNncLFtUWZFoauDLzej/WlSZxTJqWGMHTu21Iz6afLz84mKikIu\nl+Pp6fncQhZRFEHLM63M3ejR9kfuxJ3m/PXfqFqlKh+8O5TRo0dTteqfBQFNmjQhLz+blLRw7Kw1\nd8myc9O4/zCaJk0+U7tuZGTE3HlzGThwIPDsoO5ZVZYNGjRg1KhR/PLLL6RnxlPZtSX6+uakPrhN\nWLQfmTmJ7Fl1/Lm6Y0+0xfR0yz7+LCkaEOnStSN7927kwo2NyOUKlMpCBEGOm2NDanp1R0dhQOL9\nG5wL9qNRo8acOxdMpUqa/peiKDJ8+HA2btyEp2tL6ns3R0/XiKTUm4RGHuDQqW/p1GxaaRVq5N0T\nVHRqXPrnp9HXM6Gya1uu3dpDnWr90VHooVTlUaTMp0H1IRrr0lHo0bDGUP4ImMzt27cZN24cy5cv\nx83Rl9YNJpSsITWUNb/+xqFDhwkOPlMaYPXo0YM+ffqwe/dyKj9oQ0WnxujqGJCQcoOIWH+MTfSY\nP3/+M9/328bTa/ZMa4WHc1N0FAYk3L9GRMxhTE0NmDdv7r89zf8srzOHbAtgC7QBdIH1wGpAU3xI\nnUPAUODJvyzSYfVfMDIyIksspFgUkWn5B7hYFMkSC9WSiyUk3gjKudtlbuKEh3NTjA1tmDlzJkOG\nDMHd3Z3s7GxmzpzJL7/8WupdaGdrz8fjPuKLL74os/KrSZMmBATMprAoF10d9RwZHYUeeroleWHb\nd2zHx8dH4/4WLVrgXbUaIeFbaWs2We2DX6Uq4uKNjRgbGzNokOY/b+3bt0dXV4+7iRfx8eyi0V5c\nrCTh/mXeG/jsZPyVK1fi6urKggU/cevEsdLrDRo0ZNGizRpuAcXFxRw/fpyAgACKi4upX78+3bp1\nw8baluTUcFwdfLU+Jyn1JjbWtuzYsYOEhAQOHTpEcHAw69evp6XveJzt/hTdreTaHCf72vifnsnn\nn3/Onj17NMY7evQoGzZsoGmd0VR0blJ63cTIFheHehw6+S0XQzfTpuFERLGYnLwHWo3Nn2BtWQlR\nVJFfkIGOwhZdHWP0dE2wtvTU2t/MxB4zE0fi4uIIDAykYc2hVHb709fTtkIVPJybceTsLL744gvW\nrSuRNpHJZGzdupXZs2ezdMkyIu4cAf70yvzxxx9xcXEpc55vI0/WPGfOHJYsWcqtkyU/ZwqFDr17\n92L+/Pk4OWnaYUm8Gl7LkaUgCFWADsAIURQviaJ4FhgH9BcEQfNrjzoFoiimiqJ4//F/L+eh8h+m\nZ8+epCvzuEaa1varpPFImUfPnj3/4ZlJSDybpk2bEJ8SUuYR4N2ki+jqGGFmUuI/WK1SZ/R0jfj1\n11/Jzs6mZctWLFmyHEfrRnRo+hXtGk/B1KAKM2Z8Q8+evcrMOxs5ciQgcjF0k0YeVm5+Btdv76Rp\n02ZagzEo2b36ffs2isnCL+grrkbs5l7iJcKiDnHg5NekPLzJ9u2/a/0SVKFCBQYMGMDN6AOkZ8ar\ntYmiyJXwnWTnPGTsWO0G5k+QyWR8+eWXJCYmcOzYMfbu3UtoaCjnzgVrBGPh4eF4V61G+/btWb70\nF1av+o13330XV1c3OnRsT0zCGTIyEzSekZGZQGzCWUZ/OAqZTIazszOjRo0iNvYudtZV1IKxJ+jr\nmlDFvSP79+8nKSlJo33lipVYWbjirsW/VF/XBB/PriSkXCc7N42k1JsIgqxMJweA3PySNsXjBHMT\nYxsEnr0zKJfJuXPnDlYWrni6avp6mhrb4uXWnq1bt/Lw4cPS6wqFghkzZpCQGM/Zs2cJDAwkISGe\n7du3PzcYu3nzJh999BEV3T1wcnSm+zvd8ff3f+PV7hUKBdOnTychIZ7g4GCCgoJISIhn27Ztaubv\nEq+e17VD1ghIF0XxylPXjlGyZ98A+OMZ97YUBCEFSAcCgK9FUXz4jP7/d/j6+tKiWXM2BF/ATKlH\nReHPCrFo8REb5JG0bNL8pdWpJSReF+M+Gcd+v/aE3zmskUeW+jCK2zHH8XJvW+obqVDoYWVekfDw\ncL7//nuuXw+lfeMvsTJ3K73P3tobZ7s6HDz4E+vXr38cfKljb2/PunVrGTJkCBlZcXg4t8BI35LU\n9GjuxAdhZmb8XM9DHx8fLl8OYe7cuWzYsJHc3BwUCh169erJlClTyqyQBFi48Ccuh1zG//RM3Bwa\nY2/tTUFhNjEJp7n/IJpFixZpFZbVhp6e3jNtqpKSkmjZohXKIl06NP2q1AQ+PTOOkLAtbP99O46O\njhwN/h5vj86lavp3Ey9yM/oglSp5MHHiRLUxb968iZ1l2Wbv9hW8UalUREVFYW9vr9Z2/foNbC29\nyzxOdbDxAUSOB8/jUXYytrZ2xCScxsez61NirCWIositmGPYWFYurYy1saxM6O39pKXfwdrSQ2P8\nrJwU0jPjUGGGi23zMufhbFebK+E7CAsLo1mzZmptenp6L2R2v2nTJoYOHYq+ninOdvUxMtLjzOlr\n7NvfiREjRvDzzz//bXmX142enp5GoC/xenldAZkdcP/pC6IoqgRBePi4rSwOUZJrFgN4AN8DBwVB\naCS+6V8r/kEEQWDHrp10bNeeWdcuUVlmgZ1Kn2R5PrdV6dStUZsdu3b929OUkNCgXbt2TJ48mblz\n55J4/xpuDo1QKPRJSLlKTMJ5rMzdqOmlvrNbWJSDnp4eP6/+hYpOTdWCsSc42tbEya4WS5cu1xqQ\nAQwcOBAXFxfmzp3HwYMbEUURY2MTRn4wjMmTJ+Pg4PDc+bu5lRQaLFmyhEePHmFiYlIuJXgLCwvO\nnD3NwoULWblyNZGXAh+/j/ZMmrTipWyoymLx4sVkZmbTrdVcNTkPC1NnWvlO4OCpGVSpWoUmTa34\n/fffCQnbBpSYh/fv359FixZiZmamNqahoSEFhdllPvNJm4GBpkyEvoE+hdnaKxkBCgtL0op9anow\nZcovODk50aBBQ05dXkHDGsNLZUaKlPlcvrmdtPRovNz/FOB1sKmOiZENF0M30bbRF+jq/DkHlaqQ\ni6EbMTe3wNDQAJWqqMx5qIpLdlf/rrD2tWvXGDp0KO6OjWlQc1ipppco9iE67jRr1/5K9erVGT9+\n/N96jsR/D+FF4hxBEL4HJj+jiwhUBXoDQ0RRVPMkebzzNV0UxdXlfJ47EA20EUUxsIw+dYCQkJCQ\nZypd/xcpLCxk7969/Lb+N1KSkrBzsOf9oUPp0aOHpKIs8cYiiiI7duxg4U+LOHc+GAA9XRO8PTpS\n1aND6e4YQHpmPPsDv2Tp0qWMGzeONo0m4WhTQ+u4t2MDOH/9N5RK5XN3H3Jzc8nOzsbCwuIf/10R\nRZGsrCz09PReiffkX7G1tcfSuIZWr1CAiJhjXArdRGpqKiqVikuXSirq6tWrV2bF4IQJE1i9ai09\n2/6k9vfzhOCra8gpjOLuvViNgGbKlCksXrSMnm0XoaPQXO+l0K0kpAWTlJRYGtD5+fnRt29fioqU\n2FWohkxQkPLgJkpVAbVr1+Hates0rjUKF/u6CIKMtPQ7HDnzPToKA6pUbIu5qTNZ2clExZ0gryAd\nP7/9bNmyhZ079tO91fxS6RO1eYRtJSFVfR4vw/Dhw9m1w493Ws3T+pwzl1dTSDwxMdGSJ+RbzOXL\nl5+I49YVRfHyqxjzRQMyK8DqOd3uAIOBH0VRLO0rCIIcyAf6iKL4rCPLvz7zPvCVKIq/lNFeBwhp\n3ry5xre6AQMGMGDAgPI+SkJC4h8mOzubQQMH4e9/lJa+E7C1+lPTKTs3jRMXfsLETEFA4HE8PDxo\n6TseF3vtCuE3o/25GrGdwsLC51Ybvi0kJiZy5coV5HI5DRo0wMLC4pn9RVFEJpPRsOYwKrtp5koB\nJKdFcOTMHCIiIsqtoRUVFYWPT3VsrarRpPZodBT6pc+LuhfEuWvrmD9/vsZRJ0BsbCxVqlTF1rIa\nTet8iOKpoOxe4iVOhixn6tQpzJo1S+2+1NRU1q1bR0BAACqVqrTStEKFCvTt248DB/wwM7XDxNCe\nnLz7pD9KwNbWloz0DAoKC1DIFfTu05spU6ZQq1Ytrly5Qt26dfFya0v96gMRhD+D9qTUmwReWMDE\niZ/xww8/lOudlIWNjR225r7U0eKl+uRZR8/+wI0bN8rMWZR4s9i6dStbt25Vu/bo0SNOnjwJ/1ZA\nVu5BS5L6w4B6T/LIBEFoDxwEnERRTH7W/U+N4wTcBbqLouhXRp//2x0yCYn/ApmZmXTq1JmzZ89g\nZ10FCxNXcvIfEJ98BTtbO44HHMPLywsvr6oU5ZrRvJ6mA4Uoihw+8y0163hw5Mjhf2EVr5bExEQ+\n+eQT9u7di0qlAkocCAYPHsyCBQswMTEp817rCjbYmNelns97WttvxwZy/vp6UlNTsbJ63vfrP/Hz\n86NPn3dBlOFkVxddhQHJD8JIf5TABx98wKpVq8rcmTxw4AC9e/dBEBS42Pmip2NEysOb3H8QTe/e\nvdm6desL7VSKolha+ZmQkIiNjTWDBg2idevWFBQUkJGRgZmZmcZO16pVqxg7dizmpva42pfIWCSl\nhRKffJU2bdri57f/b+9amptZ4OHUHh/PrlrbH2TEciBoOpcuXZLsh95iXscO2WvJKhRFMQI4DPwi\nCEJ9QRCaAEuBrU8HY4IgRAiC0P3x/xsJgjBPEIQGgiC4CoLQBtgL3H48loSExH8QU1NTTpwIZPv2\n7VSv5YJSFoO9k4KFC3/iZngYVapUQRAEJkwYT2zCBaLjzqjdL4oiN27v5/6DaCZM+PRfWsWrIyUl\nhcaNmnDYP5C63gPp3X4hPdvOp6p7Vzb8tol27do/0yJpyPuDiUk4ozXnS1WsJPLucTp16vRCwRhA\n165duX37FhMnfYqh2SOU8ru069CEwMBAVq9e/cxj4i5dunDzZhgff/whxfI40nOvUrueJ3v37mX7\n9u0vfGwsCAKNGzfm559/5sABP9atW0ebNm0QBAF9fX3s7Oy0Hjt++OGHnD59mrbtm3D73iEuh2/D\nygZWr17NwYMHXskRso+PD8kPnq12r6enh4eHZgGCxP83r2WHDEAQBHNKhGG7USIMu5MSYdjcp/qo\ngGGiKG4QBEGfkgCsFmAOJFISiE0XRTH1r+M/NYa0QyYh8X9AcXExI0eOZN26ddhV8MLRtjbFxUru\nJV/kQfpdZs6cyfTp0//taf5tPv74Y9au2UinZt9gbKiuiJ6Wfgf/09/x008LtCaFX716lXnz5rFj\nxy4EZDja1qKmV0/MTOzJyknhUtgWktPCOHkySKqge01s2bKFgQMH0qbhRBxt1Stnc/Ie4n96Ju/2\n7V6qdybxdvKv55C9iUgBmYTE/w+iKLJnzx6WLl3G+fPnkcvltGrZkvGfjn+mFMTr5sqVK+zbt4/c\n3FwqV65Mv379XkqYOS8vD2trGzwc21Cram+tfU5eWo6u0SNu3QpXu/7DDz8wdepUjI2scLCuiSgW\nczfxIoVFORgbWpKTl46pqRlbtmymc+fXZ34iiiKXLl3izp07mJmZ0bJlS/T19Z95T0FBAfv27eP2\n7dsYGhryzjvvvLU7SCqViu7de+Dv709ltzZUdGqCjkKP+JRrRMT4Y2FhzLnzweWq6pV4c5ECMi1I\nAZmEhMS/RVpaGv369Scg4DgG+ibo6RrxKOs+RkZGLF26hKFDh77QeFFRUXh6etK+8RStFk3wZw6Y\nSqUqLV7Ys2cPvXr1okbl7tTw6lFa3adSFXExdDO3YwOYMmUK06ZNw9DQUOu4r4KAgADGf/IpoWE3\nSq9ZWFjy+eeTmDx5stZjze3btzN27Ec8eJCGoYEZhUV5KJWF9OrVm/Xr1z0zX+5NpbCwkG+//Zbl\ny1eQkfFEyLZE7X7BggU4Ojo+ZwSJN53XEZC9TuskCQkJif8shYWFdGjfkYiIKFrUH4ezXR1kMjnZ\nuWlci9jNsGHDMDIy4t13tVfbaeNJsFRQ9GzNLz09fbVK0nnz5uNgU42aVXqpXZfLdWhQYwgPMiK5\ndevWaw3Gjh49SqdOnbG2qESbRpOwtqhEbl46t2KPP3YYSGTp0qVq9/zxxx/0798fV4f6NGk9EXMT\nR5SqQmLigzngt5UuXboSEHD8b2uD/dPo6uoya9YsvvrqKy5fvkxRURHe3t7Y2Nj821OTeIN5s6WC\nJSQkJN5Qdu3axeUrIbSsPwFXh/qlu1LGhhVoXPsDnOxq8/6QoXz11Vfcu3evXGPa29tTq2ZtouNO\naW0vFouJTThD9+7vlF578OAB584FU9GpqVa5D0GQ4ebYlP3791NcXKzR/iooLi5m9KgPsbWqQttG\nk3G0qYGujiHmpo40qDGE+tUHsWzZMq5du6Z2z8TPJuFgU51mdcdiblKya6SQ6+Lp2oIW9T7h1KmT\nHDhw4LXM+Z/AwMCAJk2a0LJlSykYk3guUkAmISEh8RKsW7ceextvKlhU1GgTBIFqlTqTl5/L/PkL\n8PCopKFjpA1BEPhi8ufEJ1/jxu19ar6bKlUh56+t51F2MhMmTCi9nptbUif1xCBdG/q6xiiVyjK9\nPv8ugYGBxMTeoUblHlrFUL3cWmNsZMkvv/wpJxkcHEz0nSh8KnVV0wR7gp21NzZWlVjz65rXMmcJ\niTeNt2sfWEJCQuINITEhEVOjshOzzU2cAPCtPpTk1DAGDx6Mh4cHvr6+zxx3wIABRERE8O233xId\nF4S9dU2Ki1Uk3A+hoDCHNWvW0KBBg9L+tra2mJqakZwWjqOtdheD5AfhuLq6oaurqbL/dxBFkZiY\nGIKCghAEAWtLT639ZDIFVmYehIdHlF6Ljy8xWrcwcy1zfHMTZ+7di3ulc/6nuHr1KqtXr+batRvo\n6+vRrVtXhg4d+lxxX4n/X6QdMgkJCYmXwMbWhuzclDLbM7NLJBeNDaxoVGsEpka2LFiwoFxjz5w5\nkwsXLtCrTxcE3QR0jVMZ+cFQwsLCNAoFdHV1GTFiOHfig8jK0VQIevjoHncTzzNmzIflX9xzEEWR\nVatW4VXJEw8PD7777jsEUST46hryC7K03lNQlK1WefokMMnOLVPViJy8NKwqvJhe2r+NKIp8/vnn\n1K5dm00bd5ASJxIZ/pDPP/8Cd/eKnDlz5vmDSPxfIu2QSUhISLwEQ4YMZvjw4aRnxmNh6qTWJooi\n4Xf8MTKogLVVZWSCDHenpuzZsxdRFMtl7VS/fv1ya1VNnTqVvXv3cfTsLLw9uuJiX/dxvtl5bt45\ngE81Hz766KOXWudfEUWRsWPHsmrVKnwFWz6lBvooCOUhx+8Fk5oWQYfm09F/ytg8MzuZ5NRwevb8\novRay5Ytsa5gQ8SdozSuPULjOY+yEkm8H8q3c359JfP+p1i8eDE//vgjPp5dqeHVE4W8RPQ2Lz+D\n05dX0qlTZyIiwiXZCwkNpB0yCQkJiZegX79+eFWuwomLP5GUGsYTCaH8giwuhW4mNuE8Nav0RPY4\nP0pfz4yiokKKiope+Vysra05e/Y0HTu3IeTmFnYe+ZTdRz/jRuQe+vXrQ+CJgHLpoimVSv744w9+\n+OEHlixZQnR0tEafI0eOsGrVKt7Hiw+pRg2hApUFc3oJFZlBXZS5GYSEbSvtn52bxqmQZTg6ONGv\nX7/S67q6unw97Sui7gVxLWIPRcr80rbUh9GcuLiQiu4e9O/f/2++nX+GnJwcpk6dyqSJnwMQGunH\nH8cnExp5gOJiFQb65rSo/wkFBUWsXr36X56txJuIpEMmISEh8ZIkJCTQ/Z0ehFy+hKG+Bfp6JmRk\nJQEidbz74u3RsbRv8NU15CrvEB9fdsVlWloa69ev5+LFiyWit61a8d5772FkZFTuOSUmJhISEoJM\nJsPX1xdra+ty3efn58eoD0aTlJyIgb4JRcoClMpCevToyfr16zAzMwPgnW7duO5/iunKOlp3+g6I\nsewhBnfnphQWZZNw/zq2NrYcO34Ub291bTVRFJk5cybfffcdOjr6WJm5k1+YycOMOKpU8ebQoQO4\nubmVe+3/Fjk5ObRp05bLIVfwcGmBs10diouVxCae507cGZzt6tC8/sfIBBlnr/yKwjCV8PCb//a0\nJf4Gkg6ZhISExBuEo6MjFy9dICgoiE8//ZTQ0JtUq9SZqhXbo6/3Z9VjRlYCsYnnmDFjWpljbdmy\nheHDR6BUqrCx9KRYVLJt2zYmT57C3r17aN68ebnm5ODg8MLHYUePHqVHjx44WNega8uxWJq5oFQV\nEht/Dn//bXTs2ImgoBPo6upy6cJF6iotyjx2rUUFdnEHdBJxc3Nk8teLGTx4MKamphp9BUHgm2++\nYdiwYaxdu5Zbt25hZGREz5496dSpE3K5ZsXmm8jcuXO5fPkK7RpPoYLFnw4DjrY1cLGrS+CFxUTf\nO4WnawsM9Mx4mHnnX5y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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf0ab668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_X, train_Y = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have already implemented a 3-layer neural network. You will train it with: \n",
    "- Mini-batch **Gradient Descent**: it will call your function:\n",
    "    - `update_parameters_with_gd()`\n",
    "- Mini-batch **Momentum**: it will call your functions:\n",
    "    - `initialize_velocity()` and `update_parameters_with_momentum()`\n",
    "- Mini-batch **Adam**: it will call your functions:\n",
    "    - `initialize_adam()` and `update_parameters_with_adam()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model(X, Y, layers_dims, optimizer, learning_rate = 0.0007, mini_batch_size = 64, beta = 0.9,\n",
    "          beta1 = 0.9, beta2 = 0.999,  epsilon = 1e-8, num_epochs = 10000, print_cost = True):\n",
    "    \"\"\"\n",
    "    3-layer neural network model which can be run in different optimizer modes.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (2, number of examples)\n",
    "    Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)\n",
    "    layers_dims -- python list, containing the size of each layer\n",
    "    learning_rate -- the learning rate, scalar.\n",
    "    mini_batch_size -- the size of a mini batch\n",
    "    beta -- Momentum hyperparameter\n",
    "    beta1 -- Exponential decay hyperparameter for the past gradients estimates \n",
    "    beta2 -- Exponential decay hyperparameter for the past squared gradients estimates \n",
    "    epsilon -- hyperparameter preventing division by zero in Adam updates\n",
    "    num_epochs -- number of epochs\n",
    "    print_cost -- True to print the cost every 1000 epochs\n",
    "\n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    \"\"\"\n",
    "\n",
    "    L = len(layers_dims)             # number of layers in the neural networks\n",
    "    costs = []                       # to keep track of the cost\n",
    "    t = 0                            # initializing the counter required for Adam update\n",
    "    seed = 10                        # For grading purposes, so that your \"random\" minibatches are the same as ours\n",
    "    \n",
    "    # Initialize parameters\n",
    "    parameters = initialize_parameters(layers_dims)\n",
    "\n",
    "    # Initialize the optimizer\n",
    "    if optimizer == \"gd\":\n",
    "        pass # no initialization required for gradient descent\n",
    "    elif optimizer == \"momentum\":\n",
    "        v = initialize_velocity(parameters)\n",
    "    elif optimizer == \"adam\":\n",
    "        v, s = initialize_adam(parameters)\n",
    "    \n",
    "    # Optimization loop\n",
    "    for i in range(num_epochs):\n",
    "        \n",
    "        # Define the random minibatches. We increment the seed to reshuffle differently the dataset after each epoch\n",
    "        seed = seed + 1\n",
    "        minibatches = random_mini_batches(X, Y, mini_batch_size, seed)\n",
    "\n",
    "        for minibatch in minibatches:\n",
    "\n",
    "            # Select a minibatch\n",
    "            (minibatch_X, minibatch_Y) = minibatch\n",
    "\n",
    "            # Forward propagation\n",
    "            a3, caches = forward_propagation(minibatch_X, parameters)\n",
    "\n",
    "            # Compute cost\n",
    "            cost = compute_cost(a3, minibatch_Y)\n",
    "\n",
    "            # Backward propagation\n",
    "            grads = backward_propagation(minibatch_X, minibatch_Y, caches)\n",
    "\n",
    "            # Update parameters\n",
    "            if optimizer == \"gd\":\n",
    "                parameters = update_parameters_with_gd(parameters, grads, learning_rate)\n",
    "            elif optimizer == \"momentum\":\n",
    "                parameters, v = update_parameters_with_momentum(parameters, grads, v, beta, learning_rate)\n",
    "            elif optimizer == \"adam\":\n",
    "                t = t + 1 # Adam counter\n",
    "                parameters, v, s = update_parameters_with_adam(parameters, grads, v, s,\n",
    "                                                               t, learning_rate, beta1, beta2,  epsilon)\n",
    "        \n",
    "        # Print the cost every 1000 epoch\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after epoch %i: %f\" %(i, cost))\n",
    "        if print_cost and i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "                \n",
    "    # plot the cost\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('epochs (per 100)')\n",
    "    plt.title(\"Learning rate = \" + str(learning_rate))\n",
    "    plt.show()\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will now run this 3 layer neural network with each of the 3 optimization methods.\n",
    "\n",
    "### 5.1 - Mini-batch Gradient descent\n",
    "\n",
    "Run the following code to see how the model does with mini-batch gradient descent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after epoch 0: 0.690736\n",
      "Cost after epoch 1000: 0.685273\n",
      "Cost after epoch 2000: 0.647072\n",
      "Cost after epoch 3000: 0.619525\n",
      "Cost after epoch 4000: 0.576584\n",
      "Cost after epoch 5000: 0.607243\n",
      "Cost after epoch 6000: 0.529403\n",
      "Cost after epoch 7000: 0.460768\n",
      "Cost after epoch 8000: 0.465586\n",
      "Cost after epoch 9000: 0.464518\n"
     ]
    },
    {
     "data": {
      "image/png": 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XavzYLbeo9S9TvvhCrxOWdOFp1gxOPRXuugveeCPza/h4vEMPhYsugtdeUzFV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\n/j/YdaAvZrmDkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e3fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.796666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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bPGdsmC0bG2iBp4l0tMjTaEaRdxrK2Oqp5yYWMUWMJczOVhO5JbCGp6r2cHPu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3C5bBiDk6ltPMTe/Hcxpi9Cpeu/t+hwEDPrOg5/8Tw7D7xObtGnyOVKzF31gETSebcDoAmMICh+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\nheSYyyg+toH4pfdOir8ggMM6gEbt4xF4n6PT+E84+1kQZMQu/g5FKx9n9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lpyz6ThF9/7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d63c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train 3-layer model\n",
    "layers_dims = [train_X.shape[0], 5, 2, 1]\n",
    "parameters = model(train_X, train_Y, layers_dims, optimizer = \"gd\")\n",
    "\n",
    "# Predict\n",
    "predictions = predict(train_X, train_Y, parameters)\n",
    "\n",
    "# Plot decision boundary\n",
    "plt.title(\"Model with Gradient Descent optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,2.5])\n",
    "axes.set_ylim([-1,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 5.2 - Mini-batch gradient descent with momentum\n",
    "\n",
    "Run the following code to see how the model does with momentum. Because this example is relatively simple, the gains from using momemtum are small; but for more complex problems you might see bigger gains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# train 3-layer model\n",
    "layers_dims = [train_X.shape[0], 5, 2, 1]\n",
    "parameters = model(train_X, train_Y, layers_dims, beta = 0.9, optimizer = \"momentum\")\n",
    "\n",
    "# Predict\n",
    "predictions = predict(train_X, train_Y, parameters)\n",
    "\n",
    "# Plot decision boundary\n",
    "plt.title(\"Model with Momentum optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,2.5])\n",
    "axes.set_ylim([-1,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 5.3 - Mini-batch with Adam mode\n",
    "\n",
    "Run the following code to see how the model does with Adam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after epoch 0: 0.690552\n",
      "Cost after epoch 1000: 0.185567\n",
      "Cost after epoch 2000: 0.150852\n",
      "Cost after epoch 3000: 0.074454\n",
      "Cost after epoch 4000: 0.125936\n",
      "Cost after epoch 5000: 0.104235\n",
      "Cost after epoch 6000: 0.100552\n",
      "Cost after epoch 7000: 0.031601\n",
      "Cost after epoch 8000: 0.111709\n",
      "Cost after epoch 9000: 0.197648\n"
     ]
    },
    {
     "data": {
      "image/png": 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c1J6IiIgKU6sNcMXFQP/+rMARERFR4YlMgBOR60RktYjsFZHZInJiktu3F5FfisgaEdkn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\nIiIVRgGciIiISIVRACciIiJSYf4fRgqt3VAaNqgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d0e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.94\n"
     ]
    },
    {
     "data": {
      "image/png": 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7WFFfyjcZzSQSOUQTKxqKafG6+X3ubNw+H09V7mZFfSmt0ocewTnxmdyZOYXo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\nG7P1IzNM1hnw8XlnIy0+F9k6M3PDYvpEdPojXWf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GLlbWF/BizuLeJo/h5lSxJjpfcSvv\nLf8LW99TYVKoqQh0Meeg4dKt0kknHhJO0LrKnfY2bAEfF5Le+11RCQUXyHR20kqmzjTsAq/b7+X/\n6vdyGglcz0QUQuCWflbKnfy5bg//yT5zyFZ/tyfmcXP5Rn7m30COtNCMk2ac3Bw/kfgxqpcM8XUU\nz+PoZM9L92NrqSDMGIvT3Unll88zccUdxOYtGutljinNe9dStf5FpuRcxMSMZXi8DrYXvkLhmw8x\n+3tPjLth8iNJSOSFOCasPjdPN5XwaWcDbulnrjGa78flkq03D7zzOGZTdws+JBeQ3ntTVAkF58lU\nVnp3Ue22kT4CncfDIfDc3a1UfPEf2vatQwb8RGbOIf3MazFEjcy8w+PhfMWtrP3XV1xyfgkvtJYT\nI3XMJY5mnPyXfYQrNSwxj29LvSMhe/5V0FdUKQ75+XCyqbsFl/RzKRN6haVWKFkh03jMs5tqj520\nIXYtp2rD+Hf2GbzdXkOhs4MMlZELIlLIN0SMwCcIMVhuW98AWCj/9Gm8Xa2cf+aviI6YgNfrZOPu\nVRS/uxJL6hQ0Yaeum0rdtv+RFDeNGZMuB0CvC+eM2T/mtQ9vp3H3B0xYfMMYr3D0CIm8EEPG4fdx\nc/lG2jxuFpGIARVf2Rr4sX09T2WexoSjiCC738u/mvfzQUc9joCPmcYobojLZpJ+fET0Dgi7wCG3\n4sAhPx9Opi/3HbfA8zq72PWfu1DZujlfxqNCwef7d7Czaiczrn8c/Tj0IF7y+ul88Jaf1hXP89+O\nEv5DCQDxKj2Pps3FMMjGklq3nfc76uj0e5ioD+fs8MQxHU0yzRCJQSh5T1bxXTkRIQQBKXmPaiKU\nGvJG4Lvu7/mGHtqxe+C17xhnFUaotFwXOz5mMYb4moDPQ3PRl8yYeBnREcFmNbVaz9wp11BVv5nm\n4q9Inn3RGK9y7HB3NhGTtrTPNpVSQ6Q5FVfnyAzKH6+ERF6IAWnyONlka0ElFCw0xbK2s4Eaj53f\nMJfEnvlfZ8lkfik381zzfn6T2n9BuTcQ4LaKTVS6bJxBImY0bLA1cpN9A09MWDAuhN68sBjUKPgf\nlVwtsxFC4JV+3qeKVI2R1EGOeBkK+itmwuvHd4z6nWvw2tr5tZxLtAimOZfIJO7zbKZ28xtkn3Pz\nMKx0+Dn37UV8Zj2PV3N/S5GjA4tKwwxj1KCH7a6x1vKHut3oUBIptLzRXsV/W8r4a8Z8otVj4wRg\nUKr4SUIef6rfQxXdZMpwSrDSiINfJsxAPcypWoDZxmiUCD6gmm8QHK0TkJIPqSFepSdda2JdVxMv\ntVZQ5baRoDFwRVQ6Z1tOzMaWU5UDTVl+Xzcy4MOo79uhrVEbUKv1+Fy2MVrh+MAQnUp9y16m5Fzc\n+2Du8dpp7SgnKXfGGK9udAmJvBBHRErJ080l/KeltDeSpUbBBF0YmZh7BR4EU0NzZCwb7Y1HPN7n\nXY0UuTq5n1lkimBF/TKZzG/lVp5pKuGR9LkDrqnabeOLrib8MsACU+ywd7xaVBpuTpjIYw2F7MNK\nqjRRhBW78PJI4pxhj+QNVx1eZ/UeJsuIXoEHYBRqZstotlTsPO7jjySL73Xy2b5fsH7Ko0Par9Xr\n4qG63SwgjmvIRYOSWmys9Ozk8YZCfnuEh43R4KLIVJI1Rl5rq6DG08VEbTgPRk9jimFkUmhRah03\nxGbzdHMJ+2UH6ZgpoJ1GHPw+cSbvWmv4U/0esgnnNBIoc3bxy9od1HkcoUjdCcLBTVkqbRjGqBTK\nataRnjS/97pU37wbt7ub8OS8sVzqsONzO2gq+ITOmgKUWiNxk5dgSZ1yxPcnz72Mgtd/zbrtT5Gb\ncTYen51dxW+CQkHCtHNGceVjT0jkhTgin3Q1sKqllEvI4BxS8BJgNRV85qojAi1Syj6ipxMPBsWR\nv1Lb7W0kY+wVeABqoWSBjOd/9soB1/NscwnPNO9HixIlgqeaS7jAkszPkqYOazPEFVEZZGrNvNle\nRZPXxZm6OK6ISh92F5DhbLRQag10CO9h2zvwoNQeW4TU3lKF29aGMToVrSn6eJd4VBbf62Tl2stw\nLXlj0Pus7WoA4CqyewcNJ4swzpWpvNZVhivgH3FXkqMxMyyKmSMwC88bCFDo7EAAeQZL7xy762Oz\nSdeGsbq9iiJPOzl6Ew9GTyVHF87FxZ9wGvHcwKTev9lXZSn/at7PJZGphKs0RzljiPHAwRF/IQRp\nZ1xD4Zt/4KMND5ORNI8uWxP7Kj/GkjoVS9rJ40jisVvZ9fzPcHY0EBOZg91dyq7dH5Ay/0omLLqu\n332isuaSc96tVHy+ivLadQAYIlOYcuVvR/xaNt4IibwQR+SttmomEcFFIgMAHfAdmcNe2mjBxRqq\nOU+mohCCvbKdTTRxXcSRowIGhRIbXgJS9hFlXXjQD3Az3mpr5Znm/VxEOitIQ4HgSxr4d8c+8g0R\nXDgIv9uhMFI36IPZkTF8EZS4vCXs3beOtdSxiEQEsIPWYDfnlMuGdCx3VytFb/+JzrrC4AahIG7y\nWeScezOKERQDdzwSPyShZ/f70KJEd8hlzIIWPxL3GIu8keDjznoeq9+L1e8BIEqp5c6kfBb1jDRZ\nHJ7A4vC+9Ze77e10B7ycTUqfh7KlJLOGanY62nv3DzF+ud3bdzZmTO5pTL7sAaq+epENO59FpTUS\nN/08Ms64dkRqh4cLKSU+lw2FSo1yECUVFZ//G7+jm4vP+iPmsASklBTsf4cdG18hJve0I/p3J0w7\nl7jJZ2FrLkeh1mKMThvXv5eRIiTyQhyRFq+LSYfMMFMIQYo0oVILXvOW8Qm16KSSBhzMNERxdfSR\nHSuWWZJ4sa2Ct6ngIpmBQgjKZRdfUM9FlqOLtHetNSRh5GIyev9QF5PETtnKu9baYRd5I82CZ6ey\n5JHhu7FGZc8nYdp5/GfX+/xPUY0KBa0BO9GZc0mYfv6gjyNlgILXfkXA3sXiubcRYU6ltmkH2wpf\nRqnWkn3OTcO25v6445F4ph0yNPlITDdG8TQl7KCVWQRngwWk5CsayNCEYR4ncxeHiwKHlV/X7GAm\nMSwnDQm866/kF9Xb+WfmaUcsXdD0CF0Hvj7bnT2vtaHBxuOehXvu5L5+hh9HZy8gOnsBAb8PoVCO\nexHTun8DlZ//G3tbNUKhJDpnIZlLf4j2KJ3ALcVfMHnCeZjDgg8vQggmZ51PUcWHtBR/cUSRB6BQ\nqTEnnvgOSMdDSOSdxPhkgPXdzWyxtaIVCpaGJzJpCHPpsvQm9njbuEJmouy5ETilj31YuTg8lbPM\nCXzSWY9bBpgbFs1CU9xRC+Zz9eF8PzaHfzaX8CUNhEk1NdiYqAvnhtjso66lw+chFv1hF7FY9Ozz\nWQf9mUYSd8CPSigGbBoYiXl4Qgiyz72FuPyzaOkZoTIlcw4RGTMRQ7iJd1TvwdZSwbmn3Udc9EQA\nJk04B6/Xxe7db5Ox6DpUx+CgMBT6c8foj2mGCOaHxfCUbS9nykTi0LOVZvbTyR/jZ43KDc8V8POv\n5v28Z62l2+8h3xDBDbE5IxIFfqW1gjgM3Eh+byT8xzKf+9jIq22V3J/cf4ouR2cmSW3gLW856XIa\neqHCK/28TjnhCjUzjOPXXi1EkO2tFcCRHwrHk8XhkWgv38reN35PQsxkZs66Cae7g4LS99j9f7aD\nugAAIABJREFUwr3M/O5f+3WjkFIS8PvQqPrOzBRCgVKoaCr4FENkCnFTzh73AnesGP/fjH4QQtwM\n3EXwW78L+ImUcssR3rsIWHvIZgkkSClP2l5qV8DPXZWb2eFoJwEDLvy82FbBNdGZ3Bg/cVDHuCp6\nAj/u2sBj7GaZTMGLn/eoQgrJZZFpJGgMQxKNAN+NzWahKZYPO+pwBHzcYMxiiTlhwI7DPIOFl+wV\ndEkPZhFMGXqkn520MtswtjepzzobeLZ5P2XubvRCyfKIZG6Mm4ixnwvvSNoRCSEIT55MePLkYz6G\ns70OEMRG9X36jYvOJVDswd3ViipmZEUeHO6OAUHLrpfbKih3O0hU6/hGVBp/SJ3FqpZS3mmvwer3\nkKe38EjsHOabYgc4w/ETkJK7K7ew12HlDBKJRsdmRzO3VW5iZfpc5oQNb+1PtdtODpZegeeQPj6m\nBjd+PutsJF6t58roDEyHRDAlcGlkGv9oKuZO1pEpzVRjw4WP3yXPGtNxM0fDJwPsc3YigVxd+Ih0\nJZ8IvPzk1ex6ZOwnDxwvVeteIiYqm7MX3NX74JkYk8/ba++jpfhL4qecfdg+Qggi0qezv/oLstPP\nQq0KCsH65t3YnW1EWTLYt+YxbE1lZC27cVQ/z4nCCSfyhBDfBB4FfghsBn4KfCCEyJFSth5hNwnk\nAN29G05igQdBa64CRwd3M51JIpKAlKyhiv+0ljHfFMv0QdgcTTZE8FDaLB6vL+Qx7y4AsrVmHkua\nR8JxTLzP1YeTO8Su2Esj03irvZo/+rexTKagRsFa6rAJL9+OyTzmtRwvazsbeKBmO/lEcgOTaJZO\n1rTXUOrs4m8TFvSpPTwR/Cb1EQmApKV9P7FROb3bm9tKUCjVaM2jW7R8wB3j8ZdbeaB6OyZjDHHx\nMyhsL+XLyk3ckTCZH8bl8sO40U/JbLG1st3Rxh1MI18EHzSWymQeZgdPNe5jTtbw/q6StAZK3Z1I\nKXHh5yG204wjmKqWgudbyljb2cDfMxf2Cr1SVxc/r9pKvTf4vfPhp0np4FxLEpdHppMywlHZY2Vd\nVxN/ri+gxecCIFKp5fbEPJaeoF7GIaC7sYTcyVf1ySxYzMmEm5PpbtjXr8gDSD/zWnY9fw//++x+\n0hPn4XBZqazbSEJMPmcvuIui8g/Zuv0FEmddeEo5WQyWE07kERR1T0op/w0ghLgRWAHcADx8lP1a\npJRdo7C+YWNTdwv/bSljv6uLKJWWS6JS+UZk+qA6Sd+31rGAeCaJoJhTCMFymcaXNPBhR92gRB7A\nQlMc83NiqfM4UApBgvrwlOloEK3W8bcJC/hLQyHP20qQwFR9BD9PmHfU4ctHwxPw0+pzY1FqBj14\n92CklPyzqYSpRHEbU3t/L7nSwqPOnWyxtfbxuj0wqX48Y0mbhjEqlS+3P8m8KdcQGZ5KbdNOdpe8\nRVz+0hFP1fbHcm5mv/3bJMVNY9Hc21AolEgp2bR7FX+r/oJzLEmHRa9Gg+32NiLQ9vHeVQoFp8kE\nnnMV4w74hzVKdnlUOrd0beRZijChoRE7DzKHZBF0smiQafzKs4U32qq4LjYLbyDA3ZVb0PlU/ILZ\nJGNkO62s8hfj9vvHrcArdXVxX/U28ojkB0xGgWCNv4pf1ewgVq0bsTE0IUYWtc5Mt62pzzaf34PD\n2Y7lKA/9prhMpl/zKJVf/Ie9pe+h14YzLfcS8jLPQwgFuelnsb3wFdrLt4VEXj+cUPFvIYQamAV8\ncmCblFICHwNHcx0WwE4hRL0Q4kMhxMKRXenx82lnA3dWbabT4eWcQAqxHgOPNxTy5/o9g9rfHvAR\nTt9OSIUQmNHgCPiOsFf/KIQgRWskUWM4JoHX6fPwcUc9H3fW0+U/fMzHYEnThvFo+lw+yjuXDyed\nw98zFx6TxZJfSp5pKuHC4o+5omQtK4o/4k91u3EO4ffS7HXyUN1uqrxOKoWd1yjDLoOfLY8IwtGw\nx/F1reCCZ6cGa83GOUIoyL/i1yjMEXy6aSWvfXg7G3etIio3WCA9Ftiaymlo6yYvewWKHtEkhCA/\n+wLcAR/bbEcK4I8sRqUKJz489HWT6MSNRih6R5sMFzOMUdybOIUdopUPqGYKUb0CDyBBGJlONOu6\ngjfSdd1NNPtc/IA8MoQZtVAyT8SxgjTe76jD7h/adWC0eK2tknA03MIUskQ4E4SZH5NPPAZeaa0c\n6+WNKgv33HlCXDcGQ/y0ZZRUf0Z1/VakDODx2tm0axU+n4u4/KVH3TcsJp2c825BSj8z865gSs5F\nKJXB+5s/4EXKwAlRlzgWnGi/lWhACTQdsr0JOFK+pgH4EbAV0AI/AD4TQsyVUo7LKbEBKXmisYhp\nRHMLU3ojd9myluetJXwresKAPpQzjVFs7mrifJmGtmeGWK20UUYnlxpT+t3HGfChQAxr9OHl1gr+\n3liMt+dGqBUKfhKfx6VRacd8TP1RZvENhqeb9vF8axlnk0I+kVTKbt61VtHu8/CntNkD7t/idfH9\n8g04hJKJE87G7/fycc06dgXauV/OJIDEga+3u/NESNMejC48lhnXrsTWXI6nuw1jTBq68LhhP4/T\n2kDLvi/xe91EpM8gPHly/w8RBzbJQ11fg6/HquB6aXgiTzft41VK+abMRi0UVMtuPqaWs8MTBu3a\nMRQujExlaXgiN5avx+8+3AXXz9fjiZq8LtQoSKRvxC4dM14CWH3ufutGx5oql41sLH1EskIIcmUE\nVe4TKhlzXCx4duoJdd0YiLSFV2FrLOOzLX9BownD53MF66jOv72nTOToaIwRhCdPZk/puyTGTUWn\nMSFlgJ1Fb4AQRGXPH/kPcQIy/v7ChxkpZQn0mGIG2SiEyCSY9u1/kuIYU+9x0OB18k1y+qRmzySB\nF9nPNlvrgCLv+tgsfti9nt/ILZwuE7Dj4wvqSNeEca6lb0i70NHB3xqL2OloRwALwmL5ScIkUodo\naH4om7pb+EtjIWeTzArSCSD5n6zgkYYCMnRhTB+Drj6738srbZWcTxqXiWAtXz5RREkdT3cXUu7q\nHjD9+0JrOXYEFyz5AwZd8Cl7YsbZvPPZA6yljhpsgGRpeMIJJ/AOIITAFJcJcSNT71izeTXla59B\nI5SohZLq9S8RnTmPSZf+HMUhqdew2AnozLHs2f8/oiOzUCpUSBlgV/Gb6BQqZo9Rd2iSxsAdCfk8\n2lDAZpqJkBpqsJOhDeOm+Ekjdl6DUsVFkak83rCX/bKDbBH8DpbJTnbRyk3miZS7ulEI8BKghA5y\n+TrivYc2whQqYsbI9m0gkrQGtjnb+8zTlFJSRidpI2ArGGJ0UKg05F/xazpr9wadKzQGYieejuYo\n41MOJWvZj9n94s9546O7iIvKodPWgM3eTNbZPzrqGJZTmRNN5LUCfuDQsEIccGQ/rcPZDJw20Jv+\n0lB42JPusvBElllGNu9/IJJmp29q04GfABLdICJZmTozf5+wgH82lfCWvQKtULLMksj3Y3P7RMLK\nXd38pGIjcVLP9UzES4CPbDXcVL6Bf2efSaTq8Lb2wbK6vYp0TFxFdm+05RqZSwkdrG6vGhORV+2x\n45Z+ZhDTZ/vMntf7nJ0DiryNtlZSk+b3CjwIFhDHRefxRstelMAvUqYTNU5vomNNd+N+ytf+k3NJ\n4VI5AbVUsI0W/lG2hdotq0mdf2Wf9wuFkqxzbmbvG79l9cd3Ex+Vi7W7go7OBu5NmopxDOfhXRqV\nxgxjJB901tHl93K9Posl4Qkj2rFq9bn5rKMBCTzEdibKCARQhJUsrZn3rLX8takIABWCv1PAN2Qm\nyYSxnRY+pobrorKHvEa738t+VxcmpZoJWtOIRVAvi0zn/Y46/klhcJ4mgveoohobd0Yde+f4icRI\njFkaDwghsKTkY0nJH/jN/RAWm8GsG/5Gw841dDeWEhY3neyp52JOGtzEiPFEc+FnNBd93mebz+UY\n9vOcUCJPSukVQmwDlgJvA4jglWYp8JchHGo6wTTuUbk1IW/IXaDDQYxaxzRDJO84KpkoI4gQWrwy\nwCvsRysUnG4eXOosRx/Ow+lzjvqeF1rLCJNq7mVWb1p3tozlXv8GVrdV8b24nKPufzQaPU7S6Hsz\nEEKQJs00elzHfNzjIVLZ04KPnQzMvdvrsQMQ1c+spkPRKBR4vYdH53w+J2laI/+XPo8Yte6kS7cM\nF417Psai0HNFIKs3UjObWObLVnbt+ugwkQcQlTmbmdc/Tv3Wt2lrq0ablMszL1xP7s92j/byDyNd\nZ+JHutG7yfy6Zidlzm5+QB5W3GyiiXrspKiNtHidhAU03MpUItDyKbV8RQP/ohgAnVDy7ahMvjvA\nXMqDkVLyr5ZSnm8pwyX9QLDL/sGU6X0eiKSUrO9u5sPOehx+H7PCorggIoWwIYrwPIOFB5KmsbJh\nLxsDwcocvVByV0I+s4d5LM14ZTjdcIabgM9L7dY3aS74FJ/bTnhKPqkLrsQYkz4q59eaokg/4zuj\ncq6RJDZvMbF5i/ts624sZfuq24b1PCeUyOthJfBcj9g7MELFADwHIIT4I5Aopbyu5/VtQAWwl6Az\n1w+AJcCyUV/5Idj9PrbbW5EEa+gOvhjek5jPLRUbude/gQxpohEHNnw8kDRtWCf5F9g7mE50r8AD\nCBcaJskIChzHN2R4gs7EDnc7Phnora/xygDFWDlDN/JzzPojTqNnflgMb9jKiZF6sgmnHgerKCZR\nrWeWceCbyNnmeJ6u30xz29LeMSNV9ZtpsZZxS8oMYtS6EzZNOxr4nN1ESe1hXeKx6PE6W464X1hM\nOjnLb+19veoLWLk2a0h+tyc6la5utthbuZHJzBXBh73lpLFRNvKUtxAFggeYQ6QIRpG/yyQc0kcR\nVpz4uDQybdBzMg/wRnsVzzSXcB6pLCSedly85i7j9opNvJSzGINShZSSR+oLeNNaTRphmNDwhK2Y\n1W1V/D1z4ZAzAudFJLMoPJ4d9jb8EmYaIw+L2Eop2WRr4aPOepwBP7OMUSy3JB9Tp/x4YvpyH+cP\noxvOcCJlgL2rf4e1cgfpifMwRERQWbWZHaV3Mu3qPx3VfSLE2HDC/TVIKV8RQkQDvyGYpt0JnCul\nPHB3iAcO7izQEJyrlwg4gN3AUinlF6O36sN511rDY/V7cfQ8GeuEklviJ/U2JKTrTLyQvYh3OmrY\n7+xitjr4VDxQLd5QCVdpaDkkKiWlpBUnk1THF8W8MiqdjzrreZzdnC9TCQDvUEknHi6LTMMnA7zZ\nXs371lpsfh/TjBF8OybzuGsBB+LnSVO5s3ILD7m3o0WBmwAxKh2PpM0ZVLH8FVEZrOtu4f2vfkds\nRCZ+v4e2rhqWmBNYYj5x6/BGC3PSRMqLvqQZB7EiOG/RJwNsFq2YkvOGdKyh2KCdDNR7g+mc7ENG\n8WQR/FuNR98r8A4wmUi20cJyUnmzvYobYrMHLYSklLzYWs4C4rhSBG/gyYSRKI38zL+BjzvruSgy\nlV2Odt60VnMNuSwRwXKWJungD95t/LOphHuSpgz5s+oVKhaa+s9aSCl5tL6A1dZqkjBiQs1jXYWs\nbqvi/01YgGUEPZZHmp9fcm1Pnmr8Ya3YTnv5VpbM+ykp8TMAmJp7Ce99+Ssqv/wPU6749RivMMSh\nnHAiD0BK+QTwxBF+9t1DXv8Z+PNorGuwFDis/LFuNwuJ50IyUADvySoeaSggRWvsTUmYVRqujh7Z\nQb8rIpJ52LmHL2Q9p5OAn+DQ5Frs3BVxbHUTB+jwewggqaKbPxNsZLagQSLZbm/jueZSvuhuZDox\nJGFiXUcLn3Q0cGl0Guu7mmn3ucnVh3NtTNaw2kRFq3X8K+t0tthaqXB3E6fWc5opttfjcyB0CiV/\nTZ/HJ531rOtuRiUEi1NncYYpjtML7goJvAGIyz+buk2r+aNtF+fKJIyo+Ew00IiDaQu+OeTjDdYG\n7WQgRRN8ACqinYV83ZFYTAcALbjolh5M4muRU04XUeiYSQxrZDV1HgfZejODwSclDV4ny0nvsz1a\n6IlDT5XbBgRHPkWjYzFfDyuOEwbOkIms7aw/JpF3NHbY21ltrebb5HAWSQghqJM2/ujZznMt+7k9\n4dSo3Rtt2it2YDTEkBw3vXebWqUlO3UR2/a+hJSBIdkojhQBn4faLW/S1JNS1pqjUag0KNU6orLm\nEZe/tF8btZORE1Lknei80VZJPAa+y6TelNU1MpdyunitrXJU604uiEhhj6Od5zqKeY1S/Eic+Lk+\nJuu4bZne76gjGSO/ZA71OBBAIkb+QQFvtlVT7bX3STtdKidwF+t4sbWcucQxgxh22lu5zb6RP6TO\n5oxB1iIOBoUQzDPF9BlWPBTUCgXnRSRzXkTysK3pVEGlNTD1Ow9T/uk/ebVkPVIGCI/PZcri64+5\ngLo/G7STkRStkYVhsbxo249fSnKwUEIHL1PKPGMMexztPCELuEpmY0HLOhpYRwNXkEUNNgQQOYQo\nl0oIopRaSv2dnH6QqLRKNy24SNQciMRKtEKJoG8kXIsCn+w7R3A4WNvVQCz6XoEHkCTCOEMm8ElH\nQ0jkjRAKlRqf390j5r5+KPb6XAilGhibcUYHIwN+Cl7/DZ3Ve0hLnEOLaz/dDSXERU1EKJzs/+gJ\nmgo+Yeq3fo/yFGiOO3mvhuOYeo+TdMx9apKEEEyQ4dR6uo+y5/CjEIL7k6dzWVQ6G7qbUaJgcXj8\nsKSFbX4vEehQCgUpfH28CKmjxN9BFFrm8HVtXhsunPi5jlwW9aR8Vsh0HmMXTzQWcbopdlybUIfS\ntINHZ44h75KfE/B5kQE/Ss3wXGwP2KCdzELvwZTp/L52F//qDjZTCOBMUxz3JU+j1NXFg9U7+JU/\naOWtAM4iiWSM/JNCTjPFDanrWwjBFdHpPNW0jzipZyHxtOHmJUowKFSc0zNpYIEphres1RTQ1mvx\nZpdevqKBBSPgI+yRAbQoD7se6FDhHQFROVq8/OTV43r4cczEM6jZ+Cp7S98jP/sChBB02ZrYV/EJ\nMRPPGBfX5/aKbVgrd7B0/p3Yne1U1m7knIX3Eh8TLAVpaS/j/XW/o377u6TM+8YYr3bkOXmvhOOY\ndG0Y650tfRoSAlJSjJV83eh38wJM0luYpD/+i8vazgZebC2nym1Dp1BhxU2rdBIt9AA4pY9tNBOj\n1tHiDg7DPHBZKKQdFYLTDooYKIRgkUzib549tPhcxKr1x73GkeJEsC0bbyhUamBwjUQeuxWvsxu9\nJaFnv/45X3Era5/9ig03jH3n7UhgUqp5KG02DR4H9R4HSRoD8T0RtenGKN6YeBb/s9bwVOM+ugJe\nPqeeT6gjT2fh3mNIm14dnUmL18Xr7eW8ShkAcSo9j6bO6bWTW2iKY44xmsftu5klYzCjYSvN+BXy\nuDr0D0ZKSYHTSqGjE61QUIONYmllogjOAHRIL+tpYJ7pxOzAXfDsVO57fXxfP0xxmaTMv5IdG1+h\ntOZLDLoImttK0IXHkrFofIydtVbsIMwYS2LsVD7e8GfiYyb3CjyAmMhMUuJn0rpvXUjkhRgZLo9O\nZ01HLX9lNytketCbkSqacXJl1PSBDzBOeb2tkpUNe8kjgvNIoyzQSSsuHmQzy2UaGhR8QT0u4ee7\nsVk8ULODj6jhHJmCEAIvAfwEzdfDDnLcOzAv8OAO4FON7ob9tJSsQ/p9RGbOwZI6dVw8NY8G7u5W\nSt7/K+3lWwFQ68NJXfhNkmZddMTfwZLXT2fts/Qr9NwBP+9aa/iyuxkIRsHOj0ge0dl2I0GCxkBC\nj7g7GJVQcGlkGhdYUthga6bJ6yRTa2aGMfKYvjNKIbgjMZ9rYrLY67BiUqqZbozq06SkFIKH02bz\nWnslH1rrqQvYODMsjm9HZ5I8DB65zoCPn1dtY4u9FTUKvARQIXiUncyXcZjQsJkmvIoAN8QOj6gc\nbcbz2JSDmbDoOiIzZvTUuzmYMO17xE9Zhkp7+HdxLAimlD1IJFIGUPXT2a1SqJFe/xisbvQJibwx\nIEtn5o9ps3ikroCHfNsBiFZq+U3iDCYfgxfrSOKXEnvAh1GhOmrnqSvg5+mmEhaRyLXk9t5M3pDl\nvEcV71CBH5gfFsOP4nPJ0pn5psPKy22lbKARi9RSSDsAr1HKd2QuKqHAKt2soYo5xmjCB6glsvt9\nqMTw2rKNNVJKyj59mrqtb6HVmlEq1NRuWU10zkLyLr4XcRJ91v4I+H3sfukBcNpZMP17mAwxVNRt\nZP8nT6FQaUicvvyI+y55/XQ+27OM9VMe7d3mCvi5tXIzRc4O4qPzAMmjDXv5oLOex9PnnlTfHbVC\nwZnm4RvFEaPWsTj8yPZTGoWSq6MzR6RZ7InGYvbYrdzCFKYTjRU3z1HMfjqoVnfjln7mh0VzbUw2\nKcMgKkcb3drLuGOcjk3pD0vqVCypU8d6Gf0SM/EMaja9zt7975IUO5UdRa9i7aohwhwcutFla6Kq\ncRvJp0AUD0Iib8xYaIrj1dxYSlydSAk5evOwG5ofDwEpeaG1nJdbK2j3uzEr1Fwelc51sVn9rrPE\n2Ul3wMvigwqhAZaQxDtU8tuUmSwyx/f52U/i85gTFsMaay22gI8bDDkYFEoebyxkF23EST3ldBGu\n1HBH4pELqbfZWnmisZhiVycKBGea47g1Po84zcCp3YCU1HrsqISCBLV+3EXH2su3Urf1LWZPvoqJ\nmeciEFTVb+aLbU9Qv3MNSTMvGOsljiht+zfiaK9hxaLfEGVJByA+Jg+vz0XNhldImHbeUf/PFt/r\n5LM9d/YKvbfaqylydnDu6b8gJjIoRlra9/PBV7/nbWs1V0RljPhnCjE0vIEA71lrOYcUZopgo1QU\nOr4vJ3En6/lWTAaXRB67F3aIk4e20s3Ub38HjT6cHUWvotdZUCo1vPv5L0lPmocQCqrqt6A1RZM0\n66KxXu6oEBJ5Y4hSiGGpgxsJnmzax/OtZSwikYlEUBroZFVLKe0+N3f3U9ej64mA2A6xYjuQatUr\nVIfdjIUQLDDFHlaYPSssmnesNbT73JyjT2SFJRnzEaJ4exzt/LRyMxMw8z0mYcfLh1013OzcwKqs\nM49qwL6+u4nH6wupPTB7TGvmrqR88sdRNLWp4FMiwlOZlPm1mElPmkdF3Uaa9nxy0os8W3M5en1E\nr8A7QEr8TCrrNuL3OFANELlZfK+TlWsvw7XkDT7raiIpbnqvwAOIicwmKXYqa7tqQiJvHOIM+HBJ\nP4n0/X8OF1pMUk2b1z1GKxseTrQo3nilZvMblK99huiILCYkzKOhZS+d3fWExWeiMVho6qoFIHHO\nxSTPuQS1/uj2lScLIZEX4jC6fB5eaavgQtK5REwAYC5xREkdr1rLuC4267AGiGydmVSNkdWectKk\niTChxiV9vEopEUoNM4fgUztBZ+LWhMENxX2uuZQkjNzNjN4I43QZzX3eTbzfUcs3otL73a/Q0cG9\nVduYSASXk42PAO+5q/hpxSZWZZ/ZOxpirPG57Rh0h9dSGXWRtHUMxa75xERrisLl6sLp6kB/kFdw\ne2cVKo1h0CMQ/j975xnYVnX+4edqWJItyduW916Jkzh7LwKEsEcgjFKgLS39t2VDKaNQWlZLKdBB\nmWUHwg4hEFYC2cOJ4yTee+9tben8P8hx4izvlej55qN7j17Z8r3vPed9f787nzbwzMbLcURtR3aC\nLVlJJscpxJDF7Wbo0MqVGBQa9tkbmHWUbXmhaKUVK4mjYD05VIxld4vxhM3URsmPb5ESu5wZqdci\nSRJO4eSHXf+krqOUadc/c9qXtpyMsbM/6GbMkG9uwyqczKanLt1sgnEiyDG1HneOJEk8GD6FWpmR\ne9jKkyKdu9lGrtTCg+FTUMqG56t2yNjCdIJ6bCEHSZ7Eoj+lLdu7DYUEoeF2JjNZ8meaFMhdpCEJ\niY+bSocl1oHgHTGR6oZDdJoau8dsNhOlNXvQh5/+WmCBKYuQK1VsTv8vbR21OJ0Oisq3kV38NYYp\ny/t14b7zaQNL7lhCZc0+Wtoqu8eb2yqorMlg4TBIfQw1OaZWXq3N47W6fArMbUM2rxCCg8Zmvmyu\n4ICxCTGGEl6ZJHFDUDw7qeVVkcUB0ch3ooJ/cYB4lY554+Dv5mZ4aSndj9NhZWL8+d0PxDJJxoT4\n87B0NNBRVzTKEY4e7pU8N8dx2BKoDhMhR22R1OHSgPOWn3jrdKKnL6sTFrOuuZwSSwfzPAK5yDei\nW95hOPCWK6lzGnuMOYSTRsxMU/id9LwCUzup+CM/KjnUSAqShS8FpoHdPJ9VHmQpCwZ07skITVtB\n9b71rN/8KMnRy5DLPcgr2YjNYSFizsohfa+xiFKtY+IVD5H16RN8+t09IEkgBP7xc4hZ9NN+z5fR\neTX6yF2s/+GPRIbORAhBefUeolRaLvWLHIZP4KLc0sm29jpkEizQBZ+wK/ZUOIXg6aoDfNZcjhYF\nTuDVujxW+UfzO8OEQdWSNtrM/KEsnUOmlu6xZLU3T0bNIHCMiMVe7BeJQwj+V5fPVkdNd+3t3aGp\nfbIidHOa0y1FZu8x7HTau14+M1fxwJ3kuTkBsSodiSo9aywFBAkNIZIX9cLEavIJV3oy6RQ1a/5K\nNTcEJYxYrBf4RfBKbR6TRQDTCcSGk48pohkLK7qEWk9EoFJNua2n8LRTCMrpYIZyYBZq23+WyaYD\n5wypILJSoyftJ3+jeNPr7M/7DOF04Bs7nSmLHsDT7+Sf73TCJ3Iys3/9Ok2Fu7CZ2tGHJqENHlgH\np0LlRcolf8fR+QGN73yLBNwYEMOV/tF4yfum19cfhBC8UJvDOw1FyGUKEILnqrP4RVAiN/bj/+Sb\n1ko+ay7nehJZRCgC+I4K3m8sYKqXPwsH0UX7SHkGlSYjdzCFJHzIp5X/mbN5qGwv/42bN+B5h5rL\n/KO4yC+COpsZnVzZrdE3Xpn72mSWfjS0D4VnKr7RU5Er1WTkfMy8qTcjk2TYHVYy89YqzETdAAAg\nAElEQVSi8TbgFRg92iGOGu4kz81xSJLEI5FTuaN4Jw/Yd+IrVLRgwUfuwTORs3o4dYw21/jHkmVs\n4YX2g+hQYsWBDSe3h0w4Za3OZf5RPGTcy6eiiOVEYsfJJxRTh4lLhnFFZyCo9UGkXHxv1xaaGBPe\nkCONXKkiMHnhkMylUGtRqG8i9Nabht0GbWNbDe80FDE15UomxC3HKQQH8z/n5by1pGh8+myr90Vz\nBRPwZal0xEZvOZHsFnV80Vwx4CSv1NLBXmMjvyaVSV1OFRPx4zqRyD9NBygwtxGv7pvP7UigkGRj\npl52sEgzz4GP3A45Q4FC5Un8Ob8md/2z1DblEeAdQ21TLlabidSVD5+R18zDuJM8NyckSqVldeIS\nNrXVUGrpIMzDk7O8Q9DIxtZXRimT8UTkdDKNzezpaEAtk3OWd0iv22FL9QZuCIznrfoC1lLimgsZ\nd4eOre7ao3FtyfUvwTY1V1O6bTXNRenI5EoCkhcQOW8VSvWZ0VnWG8Ntg7a2uRyDXyKTEi8CQA6k\nJV9BVc1ePm8u73OS12a39SidcArBPhpowUJ9p4n3G4q50De836uRNVZXkhFNz+9DNPru18dSkne6\nkLbC7rZAHGIMk87GMyCS6n3raW2txX/iIkKnXoin/5ntLz627thuxhQqmZzlp9jyHCtIksQULz+m\neJ28Bu9E5/wyOIlL/SLZ2V6PQpIxVxfUXY94OmBqqWHfW3eiREFi+ELsdjOFGV/RXLyPqdf/fcj8\nYsc7w2mD1mi3og/o+T8kSRI6XTgNLfl9nmeSly/fWaoxCTtq5PyPbLZSQyRadE4l/67J5rOmMv4T\nO7df3+FolRYJOEgTSzkS50Eau193M/Tk3HsVPD3aUZx+6EMS0YeMT8eT4cKd5LkZFEIIjE4Hapl8\nXBZAByk1XDTGtmeHivIdHyAXMi5c+mfUHq6VmoTopazb9CA1B7897TX2+sOpbNAGQ4paxw81GdhT\nrSi6GpasNhM1dQeYre97V+gq/xg2tFTymDOdFHzYSg0/J4X5ksuBolp08pg1ndfr87k9pO9d18Ee\nGs72DmVNaz424SAJX/Jo4ROKWKwzDIkl2XDQ6bDRYLcQoFCfUgvTjZszHfd/h5sBIYTg8+Zy3qov\noMpmwkum4GK/SG4OSjzOGqrGauSN+gK2tdWhkGQs9TZwfWB8rzZl4xGx+xsY4g7bgdJcvI+Y0Nnd\nCR6Arz6cIP8kmksy3EneMZzIBq0vWJwOyq2d6GTK41xWrgmI5ZuirXyz5XGS45bjFE5yCtYjc1hZ\neRINxxMRrvLi3zFz+XdNNt91VhKIhnkcqcMLkbxYIELY2FLdryQP4L6wybTabbzfWcBh4ZQoDy/u\nDO2bVuVIYnE6eL46i/UtFViFE5Uk4yLfSH5jSMbjDO6g7A0hBB21hZhba/H0j8ArYPQebK0dTRRv\nfouGnC04nQ784mYQs/B6PP0jjjvWbu7A3FaHSuuP0nP86iGOJu4kz82A+LCphGers5hFEBcSQ7mz\ngw8bSqi0dPJE1Izu4+psJm4u3IbDIZiLARtOPmssZ0d7PS/GzT/tnsK3/yyTja8xJrrm5EoVFlvn\nceNWmxGlMvgEZ7g51gbtVAgh+KCxhP/V5dPmdDm7TPX04w/hUwjrqgmNUet4LmoWz9XksDn9BQBS\nPf24I2Z2vxsIEjR6no2ZzaPl+8hpbTtONkWJDPsA9O0OGJvZ09lANDqS8MWKg+3WGu4v28sLsfPG\n1Ar9k5WZbGqt4UKiScCbXNHCZ02lWJwO7gsfm16qxzLSDheW9kayPn2Ctqrs7jG/mOmkXHwvCvXI\nbsfbzR2kv34bWC2kxJyNXOZBfukm9r15J9NufA6NbygATruNwu9fpibza5wOG5JMTlDKYhLO/T/k\nfbCrdHOE0+sO62ZEsDmdvF5XwCJCuFFKAWAOEC60vNyeRb6pjQSNq1j73YYibA4njzIbb8m1crdU\nhPGwdRdfNJdzVYDbRmqwCKcD4XQgO2ZlNHDCYkq2vUdC1GKC/ZMQQlBYtpnm1lImnn3DKEU79jna\nBu1UrGsu57maLBYTylwMNGHmU2MxtxXv4J2Exd0r2pO9/Hg1bh5NdgsS4KtQDSq+efpgNrRWkS2a\nSZFcTULtwsp2apir71sjx9G8XJtLHHp+z7TuzvmZIoinTPvY1l7HQv3YeCCothr5prWK60liieSq\nH0zCF7VQsKalgJ8HJ44ZXb+TMdIOF0IIsj55DHtLPWfNvoMA33iq6w+x88Ab5K5/lomXPzhisQAU\n//gmNmMrly57Cp2Xq1whOXYZn3x7LyVb3iblonsBKPjmBWoPfs+UpEswBEykvjmfjJxPsFuMpF7x\n0IjGPN5xJ3lu+k2VzUiLw8psel6sZhHEK2Rx0NjcneTtbK9nBkHdCR5AqORFivBlV0e9O8kbBJb2\nRoo2vUZ9zhaE04532ASiF9+AT0QqAOEzL6W5KJ0NWx7DzycGu8NCW3sVhknn4B83a8TitBlbMTZV\notIHoO5HHdpoctgG7WSJnhCCd+qLmEEQN0jJ3eNRQscDtp1831rNCt+eXX1+g0zuDrNEb2Capz//\nMGYwXQShRcluapHJJW4M7J9GpV04OWRq4ack9ZBGSpJ8CRRq9nc2jpkkr8DcjgDSCOgxnkYA75FP\nsaV9zCd5I91w0VFTQFt1LmfNuYvw4CkAxITPwe4wsz3jf5jb6lEP4MFgoNTnbiUkcEJ3ggfgofQi\nJnwuhcU7ALB2NlNz8FumpVzJxPjzAQj0i0PloWPr3hcxNpafcGvXzYlxJ3lu+s1hEdJ6TKRwRG6k\nEQsCetTaqSQ5RuzHToEJOwGy068mb6SwW4zsf/f3YDYxNflyPJRe5Jf9QOZ7D5B23V/RhyYhV6qZ\nfM3j1OdupaloDx5yJVFJt+AbM21QDgl9xWm3UfDtf6k58C2iS3neP24WSeffPibqa5wOGw2522gp\n249MqSF4whJ0IUeSpDufNjDlxWtZ9at3jzvXJpyU2zo5h561TSGSF8FoKLS0H3fOUKGQZDwdPZMP\nG0v4uqWKKmcHy3QhXBcQ128nDTkSGklOs7D0GLcIBx3Y0I4hweHDCVw5HfhwJGEux/W7DlSM7QRv\n3oG7Rlw2xdTlbx3kF99jPNA3ARCYW2tHNMlzWE2YzMfbYprMLQinEwBjUyXC6SCsKyk9TFiwazu+\ns77UneT1gzNXIdDNgPFTqJijDWQtxZQJ1wW2VVh5kxz0MiXzj/KSPNsnlHTqyBNHLJN2iloKaWOZ\nd+iIx94bdTYT2aYWOhy20Q7llNQe+h5zay3L599PasKFJEYv5bwFD6LXGijb/n73cTK5kuAJS0i5\n8G6SVtyGX+z0EUnwAAq/f5naA98xNfkKLlr6OPOm3kxHRQ6HPnls1L1R7RYjGW/fS/bnf8VUnEXT\nwR/Y++btlGx5p8dx+9f68P6L1x53vlKS4SP3oJSeyVybsNIozAQP84qSSibnusA43khYyHtJS7g7\ndBIhHp5UW42811DE2/WFffK2lSSJ5b5hfEcFhcJ187UKB2sowIKDc8aQhFKSWk+SWs875FEoWhFC\nkCdaeI8CJmt8iXFrPx7HYY246vqsHuM1DVlIkqy7Bm6kUOkDaW4rI7f4O4RwJXUVtfspq96DpitW\nlc4lyt3U0tND/PDPKn3PlVw3p6ZfK3mSJE0BLgKagDVCiIajXtMDzwohfja0IboZi/w+bBK3Fe/k\nEetu/IWKFqyoJBlPRs7o0V17pX8029vreNK4lxihw4aTCjo52zuEJYOwYhpqmu0WHq/IZFtHHQAq\nScblflHcYkhG0U+1dNMHe0E2vI0XbZXZBPjGodce+R3KZQpiQmdxqOSbYX3vvmAzt1OT+TVTki4l\nNeECwNXZixBsy3iFHf+8DpnCA//EuUTOXYWHl8+Ixle6bTWmhjJWLHyIQL8EnMLJgdzP2L/1Xfzi\nZvbQ2tq/1of9F/xfD3cMSZK41C+St+oLCRdezMNAA2beIQ+lJONc75FPjt6uL+S/tTkokCFH4oXa\nHC7yjeDe0EmndKn5VXAy2cYWHjOnY0BDGzbM2LkrNLW7gWQsIEkSf4mczt0lu3nMmo4MCSeCOJWO\nRyKmjnZ4p2Q0VvEAtEEx+EROYUfm69jtZgL94qmqO8Te7DUEpixGpe27tuhQEDF7JXlfPsvOzDc4\nmL8OuVxJW0cNIBF31s0AaHxC8I2eyp6s91CrdBgCJ1LflM+OzNfRBsWiC0ka0ZjHO31O8iRJOhf4\nHMgHdMCjkiRdKYTY2HWIBrgBcCd5ZwBBSg1vxi/ix/YaCsxtBCrUnO0Thv6Y7R2VTM6z0bPZ2FbN\ntnaXhMrv9CnM0wWNGXs0IQT3lOymymziJpKJQEuGaOD9xhIUkoxbDMm9T3IUGV8qWL/iec6X3TpM\nEYNSo6PZ1IhTOJEdlYS2GxtQjIEVDXNLDU6HjdCg1O4xo7mFjJyP8FB6ER82D+F0UnjgO5qL0pn6\n02dGtNOv7tBGEiIXE+jn2p6VSTImJV1CXtkm6rI2nVBQ9Vh3jJuCEqi2GnmjNZc3yAXAW6bkqcgZ\nIy4PlNHZxAu1OawgkouJQY7EZqp5qzmXCRofLj6FFqReruSluPlsbqtlv7EJvVzJud5hPTTy0jsa\neK+hmBJLBwalhpUB0SwehYe0UA9P3kpYxJ6OBiqsnUSqtEz38h8z15KTcdu2amBkH2QOM+HS+8j9\n4hm27nvZNSDJCEpZROLy3454LIZJZ9NZX0Llnk/pNDV2hSMn/pxf4x125DqbfOFdHProz3y7/W/d\nY17+kUy87IER24k4XejPSt4jwNNCiAck12/5HmBtV6L31bBE52ZMo5TJWOYd2uu2q1Im41yfMM4d\nQ1s/R5NhbCLb3MrdpDFBcj3ZRqPHLgQfNZZwY1AC6n5qcGV8qWDKiy3sXzs8F/bgicuoTP+cfVlr\nSEu+HJlMSXnNXoorthE5/5phec/+oNIFIEky6psL8fdxNddkF36FzW7hkrOewFPjquVMijmbtRvv\npypjPZFzrhqx+BxWM2pVT7sumSRD5aHDYT35isvR7hgKScYfI6ZyQ1ACB4zN6ORK5mgDj9OJHAnW\nNZdjwJOVxHXfBJcSRqZoYF1T+SmTPKBLvzKEpd4hx732dUslj1ZkEImWyQRQbGvj/rJ0/i84mesC\n44bl85wKuSQxWxfIbEaulmw8o9ToSV35CKaWGixtdWh8Q1HpRmfLU5Ik4pfdTNj0i2gu2YdMrsQ/\nfhZKTc//RQ8vX9Ku/zttldkYG8tR+xjwiZx0RnvQDpT+JHkTgesBhKug5q+SJFUAH0qSdDWwexji\nc+Nm2CkytyNH6tFEAjAJP9aLUmptJqLGmL2TLiSB2CU/49Cm18gr3YhcrsJsbsEvdiYRs64Y7fDw\n8PIlIGkB+7I/ROWhJSxoCmXV6USGTu9O8AD02mBCgybRXLxvRJM8n6jJFFVsIyVuebcTRUNzIS2t\nZSRHnjqOY90xolTaUf9+tNgtBKE5bpXDgCcHHI0DntfmdPJ8dRYzCOJXTOxeMXtP5PNKXR4X+kac\nlqLmQ0naCjv3D9PDXn/Q+BjQ+IyNEhmNjwFN2opTHiNJEt7hE/AOH3ui3OOJ/iR5Fo5ZbxZCvCtJ\nkhN4H7hrKANz42akCFZqcCAop4PIo4zai2lHgTRk0hdDTcTsK/BPmEN9zmYcNgu+0Wn4RE4eM9sZ\nief9luzPnmLznv8AIEkyfHTHr+ZabZ3IPPXHjfcV4XTQWLQHU2MFau9g/BNmI+ulKzRq/rVkvHMP\nX/zwMLHh87BY28kr/QFdcDyBSb3XUw6XDdpAmeDpw9sdRbQJK/ouuSKbcLKPBiZpBp5g5JvbaHZY\nOZeIHluiy4nka1HO3s7GE67+uXGRtsI+rGUbbtz0Rn+SvAxgKZB+9KAQ4r2u7ds3hjIwN25Gijm6\nQAwKDa/Ys7hBdNXk0cDnFHOuT2i3ZMxYxNMvjKh5Vw/pnHZLJ531JSjUukHZHylUXky66lE66ksw\n1pfSVpVDxd51VNUd7K7VK63aTV1jLslzB/aMaG6t48CahzA2VaBUarDZTKh1gaRe9egpY9cZ4km7\n7q+UbnmXzILPkSvVBKctJ3rBtcgUfft7D9QGbTi4xDeSjxtLedKxl3NFBCrkfE8FzVj4ySC2VBVd\niZ0VR4/xwz/3tynJjRs3I0t/krwXgEUnekEIsbor0bt5SKJy42YEOaw7dl/pHh6zHXmGmaMN5PaQ\n1FOceXohhKB0yztU7PoYh92lm6YzJJB80T14+g28nlIbGI02MJqAxHkYG8r5dvtf8fOJwSnstLSW\nE5A4j6CUxced115TgLGxDLV3MPqwCSdcocz5/G9IZgvnL3qEAN9YWtoq+CH9P2R98hgzfvHCKWt4\ndIYEUlc+PODPBf2zQRtO/JVq/h07l39UHeLNTlcTSIram2dDZpGoGbgmYbxaT5jSk7W2EmKFNypJ\njl04+YQivGQKZmj9h+ojnHa4V/HcjAWk/upVSZK09KiO2mNf+5UQ4sUhiWwUkSRpGpD+WtwCkgZx\ngXQzvnAIwd7ORhpsZhI0euLVA99CPMz7L147bM0XQ03F7k8o/P4VJsZfQGzEPDo660nPXoMFKzNv\nfgm5cvDb1sLpoD53G42FO5EkGQGJc/GPn90jGbOZ2sj69Alayo5shWoDY5l4xUOovY9oMBobK9j9\nyq9YPPN3RIXO7B6vbchhw9bHSbvur3iHTxx0zH3hmbtrerVBO4xDCDI6G2lz2EjR+GAYYi/OTocN\nuxBDViuX3tHAPaW78RBy4vGmlHZasPJweBpn+4w9rcuxwmjJprgZv7TXFLD3jdsApgsh9g7FnANx\nvPhKkqTngfuFEDYASZICgP8BC4Bxn+S5GT802y183FhKRmcTXnIFK3zDWaQLHlBdmlySmKkd2q6z\n5+aFsGTt2L/QCyGo2PUpcZELmT5xFQC++gi8dSF8+t3vqc/dgiF12aDfx2U0vpCglIUnPSZ3/XOY\naotZMus2QgNTaWguZNv+1zj08Z+ZduPz3X9bq9ElsO2t7ZloeHfV/Vk7mgcdb1/pzQbtMNnGFh6o\nyKDW2gmAhMTFvhHcGTpxyLY+vYa4vGC6NoA34xfxaVMpxZYOFnsEc4lvVLd1oZsTM5qyKW7cHGYg\nV5WlwGXAbkmSJkiSdAFwEPAG0oYyODduTkW11ciN+Zt5p74IySijst3E/WXp/L3q4GiHNu5w2sxY\nOhoICey58qXXhuDpGYCxsWJE4jC31dNYsINpKVcRGTIdhUKFIXACcybfSEddEW1VOd3HegVEIpMr\nKave02OOsurdgITWMLLyHnc+bTihO8ZhOh027ijdg9MzmPMXPcxV5/2L6anX8HlLBW/WF45gpP0n\nXOXFb0Mm8PfoWdwdOsmd4PXCeFrBd3N60++VPCHENkmS0oD/AntxJYoPAX8Vo+1V5OaM4oWaHIQD\nnmAOPpJrK/F7UcHbzXmc5xtOqqdvLzO4OYxMqUKp8aahqZDY8Hnd452mRoymRsK9R8ak3tJWD0CA\nb2yP8QBfV8Jmaa2FsBTApf8VMvV89qd/gtVmJCRwAnVN+Rwq+JKgCUvQ+Ix81+f+tT5wEr/bb1ur\n6XTaOHfWrXhpXHqME+LOo72zjg/Lt3BjYPyQiPrahZOt7XVkdjahlSs51ydsTDlXuBk81s4WGvK3\n47Ca8Y1OQxsUM9ohuRmjDGS7FiARmAFUAKFAEuAJdA5RXG7cnBIhBD+213Ix0d0JHsASwlhHCT+2\n1biTvH4gSTJCp11I7rbV6LyCiAmfS4exnl0H3kap0hKYcsKeqyFH4xeGJFNQVXcQX/0RE/KqugMA\neAZE9Tg+bunPkXtoyEv/nKzCL5Er1YRMO5/YxTeNSLwn4kQ2aABVViNeap/uBO8wgX7x5BZ/i9np\nwFM+0Euyi3aHjTuLd5FlbiEIDe1Yea0uj3tCJ/UqiOxmfFCT+Q15G/4FwolMpqBo4ysET1xK0vl3\nII2CEPdo4bCZKd32HrWZ32Izt+MdPoGo+dfgEzl5tEMbU/T7iiJJ0n3An4CXcLlexANvAZmSJP1E\nCLF9aEN0M15xCMHnzWV80VRBq8NKqqcvPwmMI/Yo2y0hBBnGJg4am9HLPViqN6DvY8G4EAL5MRUH\nErg8Ld2Lyv0mat4qrB2N7M5cze6D7wCg1geRetWjKFQjsxIkyWT4x89mX86HCOEkNMhVk7c3+wN8\no6cdt2IhyeTELLyeqLlXYzU2o9R4D0mDyFBwrA1atEpLR0MRbR01PTyHq+sOEqj0RNPPG3Snw0aL\nw0agQoVH17kv1eZSYu7gPqaRKPlgEQ7eJ5+/VR1gujbAvaI3Asw7cBf3D1PDRWdDGblfPU98hKt2\nVqlQU1i+lR37/4dXUBwRsy4blvcdapx2GxV7PqHu4PfYzZ3oIyYSOXdVn1ckhXBy8MM/0V6ZQ0LU\nErSeARRX7iDzvQdIvfJR/GLGtpfxSDKQ7tpq4GdCiC+PGlMCjwO3CiHGxhV2ELi7awePEII/V+zn\n69ZK0gggAA37qKdDsvHPmDlM8PTB6LBzX9ke0jsb8USBGQcekoxHIqayUN/79uB9pXvIa2/jIWbg\nKbmKzXeIGl4ii39Gz2HaGJF3UG+8nDufHhtK833B3FZHe1UuCo0en4jUEVkdcFhN5H/zX+qyNiGc\ndiRJDgiEcEJXF27SebeOqL/tULHxCpcNmsXpYFX+j9g89EydeDVar0CKyrdxqOALbjVMYFVA325w\nnQ4bz1Zn8XVrFXbhRCf34Br/aH4SEMd52V+zVIRzhXSkHtEiHNzJFn4SFMeNQQnD9TFHFLtwsrO9\nnhqbiRiVjqlefmNCBHzua5NZ+lHvYtoDpXDjq9Tv/5aV5z6LXHZkjWZz+n+pNZYx8+ax3/cohJOD\nHzxCc2kGMWFz8FL7UVy1E6OllbRrn0IX0vt3tKl4LwfWPMSyOXcTFuxauXM6HWzY9gQWD4mpP31m\nuD/GsDBWumsnCSEajh7o6rK9R5KkdUMRlJvxT5aphQ2tlfyMFBZIrtqoy0UsT4p0/lOTzb9i5/JS\nXS6HOlu4lclMwZ92bLwpcvlj+V4+SjqrV6eJW4KTuKVzO/c7dzBVBNKChUwaWaYPYaqX3ynPHUmS\n/7oGxpFellofhFof1PuBQ0j253+jtSST6SlXEuAbR1X9QTLz1hKYvIC4s36JSjc2EvaBcLQ7xnPR\nM/lTRSYbdz0LgEqm4KbABK7yj+7zfA+U72O/uZ3JKVfi5x1FRW0GLxd9jU04MQoH/qh7HK+S5OiE\nB+0O21B+rFGj1NLB3SW7qLKZkCPhQJCs9uZv0TPHrDvNUGHtbEbvZeiR4AH46MIor98/IjHYzR3U\nHPweY0MJKl0gwZPORq3vu49wc/E+morTOWv2HYQbXCtukxIv5ovNf6J481tMvurRXudoKTuARuNL\naNCk7jGZTE58xEK2Z7yK025F5rbbAwbWeNFwitd+GFw4bk4XdrTXo0XJPI6sYKkkOUtEGG8Yc+l0\n2PmiqZxlhJMmuWRL9Hhwo0jmLrGVb1qqel3ZiFbreC1+AasbisjoaEIrV3Cv7yQu8I0YE0/1bvpG\nZ0MZjQU7WTDtFmIjXE0fQf6JyGUK9ud9RsK5vx3lCI8ghKC1/CCd9cWodAH4xc3s1ULtaKJUWl6L\nm0exuZ1Wh414tQ5tP87PNrWwu6OeJTNvJTJ0BgChQanIJBnvl3zHBLUP2801LBKh3U0c+aKFOkxM\nHkMPPgPFKQS/L92DZJPxMDOJREsOzbxkzuKxiv38PXrWqMU2Erp4uuB4irJ/pNPUiJfG9eDjFE7K\natLRGYZ/lbazoYzM1X/AZm7HVx9JfccPlG1fw4TL7sc/bmbvEwDNJfvw8gwgLPiIGIdCoSIxcjG7\nD61GCOcpRcwBFB4udxuHw4riqMTeZG5FJvc4o2oTe2NwVb5u3JwEhSThwIkDgYwjCZcVJzJc2y1G\n4SCEnjVCWkmJXihp7nJc6I1QD0/uCj1zXClORzrrigAIN/RUYAoPTmNf9ocYmyrw7uqoHU1sxlYO\nfvgn2qpzkckUOJ12VNoAUlf+EW3wqeValn60gPUr9nbX58UcVZfaH3JNrQCEh0zrMR4RMp2swq+4\nMDicp6sO8hR7mSsMNGHmeypJUXszXzeyq7PDwb7ORsqtndzPdKIk1+8wBT9Wijhe7cimxmoacnHp\nvrK3oRgY3rIMw6Szqdj1MRu2PsGkhAtRKbXklW6ksaWEycv/MqzvDZC3/lk0Ci8uOvsRvDR+2Gwm\nfkx/gZzPn2bOb95ArlT3OodMocTusCKEA0k6koJY7aauB6beH9ADUxZRvPkt0rPeZ8bEa5DLlTS1\nlpFdvIHAlEXuJO8o3MaDboaFxXoDJhx8SSmH6z5bhYVvKWe+Lhi9XEmUh5Y91HN0XWihaKURC8nu\nWsgzBg+dayW3ubWsx3hT188qrT9CCBoKdnLwo0fZ+8Yd5H/9b4xNlSMaZ96Gf2Fpqubsufdy3YWv\ncvHSx/FSaDn00Z9xOuy9nn++7Fbmvja4zr8Ahesm2tpe1WO8pa0SCYlFegNPR83CQyPxJrl8L1Vw\nnl8Y/4iZPSCxZSEERocdxxhpZGroevgLx6vHeASuWs1Gu3nEYwJXLd5I1N0q1FqmXPskysBwtme8\nxqbdz9Nib2biZQ/gGzVlWN/b1FJNW3UuaUmXd3eIK5UaZqZeh93SQVNRei8zuAhMWoDF0sbB/C+6\nr/1tHbXkFn9HYPKCPu3CaHwMJJxzC7nF3/HB17exduMDrNv0IAqtL7FLfzbwD3ka4l7JOwOosRrZ\n0l6HQDBPFzwiHXbRah03BSbwv/p8dlFHoFCTQzNauZLfGlKQJImbguJ5pCKD/3CQOcJAAya+pJR4\nlY4FfWi8GC9kfKlg42tbhrUgezzjHT4BT/8Itme+zoKpN+PvE0t1/SHSs9fgF1k3vXgAACAASURB\nVDMdtXcQJZvfpnTbavx94wjShlKZvY3ag98zedVj6MOShz1Ga2czDfk7mD3pp4QGuVaOffThzEv7\nBes2PUhzyV7843rfKjy6Pm8gzNYGEqDUsGPvS8ybfgt6bQg1DVlk5nzIQn0wvgoVs3WBzNYFYhdO\n5EgDKl0QQvBJUylv1xdRazehkym5zD+SnwUmopSN3tpAQpfVYAYNzDlq1SyDBjwkGRGq0WnMud3m\n+k4IIXDaLcgUHr1uOQ4UjW8ok1f9GZupHafdgofWf0TKUxwWIwBqVU8hbI3a9UDusPZtq1obHEfE\nnCvJ2PEBB/I+d53rtKHS+hOz+MY+xxM69QK8IyZRl7URm6kdQ9hVBCYvcNfiHYM7yTvNeb0un1fr\n8pCQkIDnqrO4PjCOXwYlDfuFYaV/NNnGZnZ1NlJFJ0EKNbeFTCBc5XoKP8cnDIcQvFqXx79tB5Aj\nsdTbwO0hQ2fxNFbY/rNMpryY6lbBx3UjbCnLpO7QRhxWE96Rk0i+6B6yP32S9T/+Cdd2jUBnSCTp\ngjswtdRQuu09piRdxpRkl0SEzW5hw9bHKfzupRHppLMaW0E48fWO6DHuc9hCrb2pz3Mt/WgBmw6c\nw7ZJf+93HEqZjL9GTufu0j189v19KGQK7E47KZ6+/D50Uo9jB/M/9E5DES/U5jCXYC4hhmJnO+/U\nF1FrNfHHiMHLUziFIM/chl04SVJ79zlxjFXrmK8N4q2OXJqFhVj0HKSJryhjpV8U+iG2dOuNtBWu\nFdw/fOZN1d61VO74CFNHAyq1HsP0i4iat2rYtg6VGh0wsG3/geAZEImHxpuC0h8I8kvsvn/kl24C\nJLwj+lY2I4STztoiZDIF0WGz8NT4UVy5A6O5BUt7Aypt32tHvQIiiVl0wwA+zZmDO8k7jdnRXsfL\ndXlcSBTnE4WExNeU8WZ9IckaHxbrh297wep0cGvxDmotZi4iGm882Gqv5o/l+3heoSKtqwj8PN9w\nzvUJo9luQSNTDFoM1s3Yp2jja1Ts/hi9NgS1Sk9h3otofMOYct1TdNYVYW6twzMgEu/wiUiSRGX6\nWmQyORPjz++eQ6lQMTHuPDanv4C1owmPftwYBoLG24BcqaG8ei9BfkcK3Ctq9gH0WpN3LEvuM7Hp\nwF0DSvSSNN58lLiEre111NnMxKt1TPMautUcs9PBm/UFLCOc66REAOZgIER48mZrLjcGJRA5iBWz\n9I4GnqzMpMrmWvnxlXvwW0MK5/mG9+n8RyKm8o/qQ3zaUoQNgUaSc41/LDcHJw44poFwdKNF2bb3\nKNn8FnMxkMoEisxtbNy6Gmt7A4krxk9n/amQyZVELbqe/A3/wmRpJSxoMo0tJRRVbCM0bQUan77d\nT5qL93Z1197ZXYc7KfFi1v/4J0p+fJPJq4a/tvBMwn1HPY35vLmcKLRcRmz3DeAiYjggmljbVDas\nSd7GtmoKLe08wkwiuwqkF4oQHiOd1+ryeD5mTvexMknCvw8Fu27GP22VOVTs/pjpE69mQtwKJEmi\npb2Sr7Y8Rtm21SSc+5uTnjuaVWFyDzVhMy/h0Lb3cQo74cFTaWotJTPvM3yjpvZJ2+tYltxn4pmN\nl2Ne+nG/z/WQyVnqPTy2baWWDjqddubQs2RiLgbeJJdDxpYBJ3kVlk7uKd1NjNBzPckokfO1o4w/\nV+4nUKlmujag1zk85QoeCJ/CrSETaLJbCFKq0chG9lam3nh5d4Jntxip2L6G5USwSnJ9D+ZiIEho\nWJ35NZHzVqEeIVvA4SY0bQVKtZbyHR+Snr0GlS6A2KU/J3zmJX2eo7kko6u79kgNocncgqfKh6qS\nfVTu/YLgiUtHTID9dMed5J3GNNoshOB13BN+KF5U2zqG9b33dzYTgbY7wQOQSzJmi2A+7iwa1vc+\nnXFpVH1HR00BHlpfDJPOxWG3ULHzIzpqClB6+RKSdh5BE5aMSRmZupzNaDR+TIg7rzs+H10YiVFL\nyM3+4YRJnl/cLAq+fYmsgvU9tmsPFX6FPiRp2FfxDhO94DokSU7+nk/JLtyAJFMQlLKI+HN+PeA5\n73zaMOBEb7g4vOXZgJk4jjRA1eNKarwVA98S/bipFA8h5zamoJJc25i/FBOpxcR7DcV9SvIOo5Mr\n0Y3w9iwcL27e2VCK3W5h7jGdtXMxsJp82ipzTpskDyAweSGByQsHfL6ru9bS3V1bXLmDLekvopAr\n8dGFU/DtC1Ts+IDJ1z7Z59VBNyfHneSdxiR7erPBVIlZ2FF3tapbhYODNDJfc7ycQovdSoPdTIhS\ng9cgL546uZJWLNiFs0dtUCPmUbkwnw4YmyrJXP0HrMYW/L1jqDPupnzXJwBoPQOICk6jub2SnHVP\n01FbSNxZvxjliI9HOKwoFerjitKVCg0Ou/WE52h8DETNu5r921ZTUbcfH20YlfUHsDnMTL7wsX7H\n4HTYaSrcRWd9KSrvIAIT5yP36H0lWZJkRC+4lojZV2Bpb8DD03tIHDjufNrAlBevZdWv3h30XENB\niIcnaZ5+fGIsIlxoCZO8aBEW3iYXf7mKmV59F749llJLB/F4dyd44FrJTxG+ZFpOKsE66qStsKO5\nchq321LZ/3TPulpllxxOPWYij6qRa+hKihWano0KZzqByYso276GA3mfkxSzjG37XiEqdAbz0n6B\nQqGivbOOr7c9RcHX/2FSH4SR3Zwad5J3GrPSL5ovmsp5SuxjuYhAhsTXlNMp2XoIDXc6bDxddZBv\nW6txIlBJMi7xjeQ3ISkDLt5e7hPG2w2FrKGAlSIOJTKyaGYzVVzp2zf7ptONZ5UHWcrAO2wLvv4P\nSqHgwrOfxkvjj8NhY3vGaxRXbue8BQ/iqXbdfA7mf8He3e8TkrYCT78wnHYrddk/0laVi1KjI3ji\nWXj6963+aajxjZ5G1b71VNcfIiRwIgA2u5mC8s34xUw76XnRC3+CLiSR6v1fUddRi1/KPMJmXIqn\nX1i/3t/cVs+B9x/E2FSBSqXHYmmneOOrpK78U5+3XOVKVb/ftzf2r/WBMZToPRA+hduKd/CQbSf+\nQk0LFjQyOX+LnDmo7toQDw2bqO3x8CeEoIhWQpSjo2/XG91WZR+d+HVP/3C8DQl8WFtEuPAiWPKk\nWVh4WypA7emPb9TAZXNMzdXUHPgGa0cT2uA4glPPQqHy6v3EMYw2KIao+deyf+u75BR/i8NhZeak\n67tFjXVeQUxOvJjtGa9iM7ai9HTLaQ0Gd5J3GhOu8uK5mDk8W32Il0xZACSrvXk2ZHYPMdY/lu8j\ns6OZVcQTi55DoomPmkpwILhzgELDsWodt4dM4LnqLLZRgycKGjAz1dOPG4Pih+TzjTe2/yyTTQfO\nGZAqvtXYSnNpBvOm3tytdC+XK5mReg1FFduorN1PQtRiAJJjzyEj5yOaitJRqDzZ/+4fMDaV46OP\noNHcTNmOD0hc/ltCpiwf0s/XF/zjZ+ETOZnvdjxDTNgcNGpviit3YrZ1kLjwJ72e6x8/OEeD3C+e\nQTKbuWDxo/j7RNPeWc+P6f8m65PHmHXLq6Mqono40Xvi0ze7RZNHi1APT95JWMymthoKze0YPDSc\n7R066FX4S/2i+KypnJfI4jIRgwo5Gygnj1aeDJg+RNEPHX31ok286B4OrP4Df+jYgZ/kRbMwolB6\nknrZQwP+TtVlbyZn3d9QyFXotQYKD35P+Y4PmHzNE0P+kDGSCOHEKyAK74hUOuqKkUlyVMqe9Xea\nLpkWh82Ce99ncLiTvNOcCZ4+vBQ3n0abGQEEHNPgUGhuY0dHPbcwkVmSq24kDm9kQmJtUwm/CEpE\nP0DdoSv9Y5irDeKb1iqMTjtTvfyZow3stlty03ecNpcIrMqj5/agUumJTCbHfpRDiNPpQAiBJJNR\n+P0rODvbuGjpY/jqI3A4rOw68A75G/6Fb8y0fnlODgWSTE7qykeo2P0J1Yc24mg24R09iZS5q/AK\niBzW9za31tJSlsmC6bfg7xMNgM4rkDmTb+SLH/5Ic0kGfrGjm2jsX+vD+bJbWb/i+VFP9Dxkcs71\nGdpkIl6t55GINP5aeYAHnHWu95Fk/CY4mYXD1AjmFKLf15z3X7wWgPs/6pvkkadfGDN++TL1uVsw\nNpTh7x1M0ITFA151s5nbyV3/D6JCZjJv6i9QyD3oMDbw9banyN/wb6Zc8/iA5h1thBDkfvEPag99\nT4BvPP5eYdRZ8iiq2E585MLuY/JLf0TtbUCl73uNppsT407yzhBO1r1aYG4HYDI9DeAn48/HFFFm\n7SR1EOKS4Sovbgoafk/F0x2VPgCNTwh5JRsJD57SXdNWWLYZp9OOX1fSIoQgM/cTQOAXO53C715m\navIV+Opd+m5yuQczJl5NUcVW6rN/JGL2FSP+WeRKFVHzriZq3tUj+r42YxsAeq+eyYRea+h6vXVE\n4zkVYyXRGw6WeYcyXxfM3s4GbEIw1dNvwA+SJ+OwmPN7DcVU2owYFBquCojhSv/oXhO+91+8dkB6\nlnKlCkPqsoGG3IPG/J047VZmpl6LQu763Wg9A5iSeDFb972MtbMZDy/fIXmvkaS5ZB+1h75n/tRf\nEhfpWiH9fsczbM94lbrGPHz0YZRV76WuMYeUi38/bILSZxKn3xXETb8I7kr+yuggkSMXtlJcyV+g\nwi1tMhaQJBkxS24i69Mn+HLLX4g0TKO5rYLiiu1IMgXf73yGkIAUWjqqaWuvInbpz1FqvBFOOxpV\nz5oWhUKNQqHGbjWO0qcZHTR+YciVakqrdhHgG9s9XlK5EwBdyMjqrPXG+bJb2fjalgG7Y4xl1DI5\n83TD13H6Wl0+r9XnM4dgziGSfHsL/6zJosFu5jeGE/sgp62wc77sVlg7bGH1GYfNjCTJ8Dhm5f6w\n24TDaoZxVppnt3RSl7UJndZAbMT87vEls25lw9YnKarYCpKENjiO1CseHnRphhsX7iTvDGeypx8x\nHlresOZwk0jpVpD/iEJSNb78reoA2aZWfOQeXOQXwUr/6NPOjWK8EJg0n0lXPUr59jVkFqzDw8vl\n0xiQOI/q/V/RXl2AJjKJmCm34dOlPq8NiqWgfDMxEfOQdf3dKmszsFja8IkcnI/qeEOh8iR81uUc\n2vouVpuJsKBJNLQUk1X0FYHJC0etGeVUDNYG7Uyk3WHjnYZCzieKlZJLpHoRoQQKDWsairk2IBbf\nriJ/OOJacb5s7IgW+0RMQggHhWWbSYxeCrhWJ/NKN6HWB6H2Pl4dYazSUVdE4Xcv01Lm+g5768J6\nyDvJZAoMAcm0GKuZd9t7oxXmacu4TPIkSfoNcDdgAPYDvxNC7D7F8UuAvwMTgTLgMSHEGyMQ6phH\nJkk8ETWDe0t387j1iMF0tIeWbFMLoXixmDBqHUb+XZNDlrGFRyNP3gXp5tQY730KBnEz8YuZdsIu\n1NiTeD5GL7qBgx/9ia+2/IXo0Fl0dNaTV7YJ3+ipZ1ySBxA1/xrXat6uj8kv3YhcqSF02oXELPrp\naId2UgZjg3YmkmtqxSKczD9Gt24+IXxKMYeMLd3e2Ee7VowlvAKjCE5dxs7MN6hrysdPH0FZzV7q\nGnNJueieUW0Q6g/m1lr2v3sfXipf5qb9nKbWUnKLv6Wq7gChQS4bPrOlzdVd38+VO6fdSu2hjTQV\n7UGSKwhMWkBA4lz3Fu8xjLskT5KkVbgStl8Cu4A7gA2SJCUKIY4TWpIkKRpYB/wHuBY4G3hFkqQq\nIcQ3IxX3WCZC5cXbCYtJ72ygxmoiWuXFM1VZxOPNXaR1r9xNFH682pbNNcYWUjzdHqwDIeNLBetX\nPD9iqwb+cTOYfNWfKd26mvSsNSg1OsJnXUbk3KvHpFjycCNJMiJmX0H4zEuxmdpRqL2QjQPdxsHY\noJ1paLv+ns24xOAP04ylx+tHu1aMRZJW3IZXQBTVGV9SWr0br+BYUlc+jH/c4LYxnQ4bbZU5COFE\nH5qMXKnq/aQBUpn+OTIhsWLBg3goPXE6HbS2V/Hdjr8TYZiGWqWntHo3Qi4jav41fZ7XYTWxf/X9\ntNfkE+SfiN1hJSv7cQKTFpJy8fhJgkeCcZfk4UrqXhRCvAkgSdItwAXAz4C/nuD4XwNFQoh7u37O\nlSRpQdc87iSvC7kkMUvr6rRss1vJt7RxMxN6bM0eVnDf1dHgTvLGEb7RafhGp412GGMKSSbHw2t8\nfYcHY4N2JpGk1hPtoeUDayG3Ci98JRWtwsL75BOq1DDJ0/c414qxiCSTEzH7CiJmX4EQAqfNjGyQ\nDSrV+zdQtPEV7BZXPa5SrSNu2S8JTj1rKEI+jvaqXEIDU/HokkiRyeQsm3MXG7Y+TmXDIdS6APwn\nLiZi9hWo9X3fgi7f/QnG+hLOX/Rwd31tadUuftj9LwLz5g/KkeN0Y1wleZIkKYHpQHf/uBBCSJL0\nLTD3JKfNAb49ZmwD8I9hCfI0QCHJkAFG7D3GLTiw4UQ1CDFUN25GCuF0IIRzXKzU9ZWxaIM21pAk\niYcj0rijZBf3OrZhwJMajGg95Hz132tZnbT8ONeKsUx15teUb1+DqaUahcoLw+RziVn0034lfMLp\nIHvd36nP/pHDLtA6LwM6bTA5XzyDyjuou453KFF6etNWX9NjTC5XIpMp0IclM3nVXwY0b0P2FqJD\nZ/VooIoKnYW/byz1OVvcSd5RjKskDwgA5EDtMeO1QNJJzjGc5Hi9JEkqIYTlBOec0XjKFczVBrGh\no4w0EYC/pMYpBB9ThAPBUv3wGKO7cTMUmFvrKNr4Gg352xFOOz6Rk4lZfCP60JNdIsYXY80GbSyS\nqPHmvcQlfNNSSZm1kwU3JvC+7efcl6uD3IHPa7cYKdv+PnWHNuKwmfGJnEzU/GvQBscNXfBHUbl3\nHQXfvEBU6CwiYi+mua2C7L3rMDVXk3rFQ32ep3T7+zTk/MjUlJXEhM+hraOWPQffpaW1DL0ulMr0\ntcOS5BmmnMvBD/9EZu5nTIxfAZKMnKJvXLWF838/4HmdDhuKEyg/KOVqbA7bYEI+7RhvSZ6bEeL2\n0In8pmg799m3Ey+8qcdEExZuD5lAsMfYtB9y48Zmamf/O/ciszuYmnwFSoWavNKN7F/9B6b+9Bm0\ngdEDnlsIJ63lh7B2tqAzxKHxDR26wPvJWLNBG4vo5Eou94/udq1Q9vFuZ2yqpDrjS0zNlWh8w47Y\nAzrsHHj/QTrrS4iPWIhG5U1RxTb2vX0PaT/5G7ohTvScDjtlW98jLnIh86fe3D3u5x3J5vQXaK8p\nQGfo3T1IOB1UpX9OUvTZTEq8CACtZyBLZv2OT7/7PQZtCB2NlUMae3essTOJnLuKjO3vcyB/HZIk\nYbebCZt+yaBW2/ziplNycBOTEi/utnNsai2lpjGbhGn/N1ThnxaMtySvAXAAxwosBQM1xx8OXeMn\nOr6tt1W856uz8JL3/BWd4x3KOUOsAj8WCfXw5M2ERXzRXE6WqYVJch/O9w0nSeP2ERwsGV8qmPJi\ny4AEV0eSpuJ9lO/4gM66Ijy0/oRMXUHo1PPHdPdadeYGrJ0tXLrsKbSeLrX8uIgFfLbxfsq3ryHl\n4nt7meHEdNaXkPXJ4xibj9wMg1IWk3T+7YOukxoo+9f6sP+C/2O98/QUTR4K+ts921i4h6xP/oJS\noSHAJ4b68m+p2ruOiZc/iN1ipK06l/MWPkSQn0vgfULceaz78WFKt7xD6hV/HNLYza21WI3NxIT1\nrESKCp3Flr0v0VaV06ckz24xYjO1ERzQcyVbrw1Bo/Khua0CXfTQr+KBa+s8ZtFPCU5dRmP+DgQC\n/7hZg3a3iZi1koacrXy+6UFiwuZgt1sortyBNih22OoLh5q6rE3UZf/QY8xuHnrt0nF1ZRBC2CRJ\nSgeW0SVZKblaBJcBz5/ktO3AimPGzu0aPyW3hkw4o5ManVzJ1QGxvR/opt+s+tW7MEBl/ZGgPmcz\nWZ89RYBvLBOjl9PcVkbBN//FWF9KwvLfjHZ4J6WtIotg/6TuBA9AoVARHTqTwoo9A5rTabdx4IOH\n0cg0LFrwIN66UMqq97DzwFsoPX2IP/uXQxX+gDid3TEGinrj5QD9SvCcDjv5Xz6HwT+FJbNuRSH3\nwO6wsmnX8+Stfw7fuBn46CO6EzxwfbfiwueTWfD5kH8GhVoLSHSaeopGGM1NCOFAkvXt761QeaLU\n6KltyCUq9EhnbltHNSZLCwDJ0y8esrhPhKdfGJ5D6K6j0gcw9af/oGznB5QWuiRUwmZfRsSsy5Gf\nxN1prBE0YQlBE5b0GGuvKWDvG7cN6fuMx6vCM8DrXcneYQkVT+B1AEmSngBChRA3dB3/X+A3kiQ9\nBbyGKyFcCZw/wnH3wOx08GVzBTs76lFKMpZ4G1iiD0F+BspanKnckGjmztEO4gQIp4Oijf8j3JDG\n0lm3da/cZRduYHfGO4TNvHTMGqQr1Do6a8pc3r1H/S91GBtQaLSnOPPkNBbswNLewPKznsBH5/rc\nCVFL6DQ1cSjzK2IW3zCsMhR94XR2x+gP3a4VT/f/3LbKbCydTaRNv7XbSkwh9yAt+XLW//gIdosR\ni60Tp3B2C4sDWKztw5JYeHh6o1B5kZH9Mf4+Mfh5R2GytLEj43UkSYbd3NaneSSZnNDpF5G7dTUa\ntQ/RYXNo76xl14G3kCQ58ef8Gp/ISUMe/3Cj0geQcM6v4ZzRjmRsM+6SPCHEGkmSAoBHcW27ZgDL\nhRD1XYcYgIijji+RJOkCXN20twIVwM+FEMd23I4YnQ4bvyveQZ65jWR8seDgj237WKqv5k8R09yJ\nnptRxdRSg7mtlqQJ1/fYmk2MXsruQ+/SUrp/zCZ5hknL2H/wWzLzPiM14UJkkpySqp2UVe8hdunP\nBzSnubUOpVLTneAdJtA3DofNjM3UhlwZOBThD4oz2R1jKFwrnA7XHMpjCvoP/+wdnkpD7hYO5q1j\nUuKFSJKMxpZi8st+IGjKuQN+35Nh7WjCbjGiUOlYt+khvDT+mMwtyGRKtF5BmJqq+jxX1NxV2E1t\nZOz7hH3ZHwCg9g5m6k//js7g9hY/nRl3SR6AEOI/uMSNT/TaTScY+xGX9MqYYHVDMSXmDv7ITKIk\nHQB7RB3/aTvIsrYalnq7u1fdjB6HV6Usto4e41abEYRANsqrVqfCJ3IyUfOuYf+21WQX/T975xkY\nV3Wt7edML9JoVEa99y7LRXLv2PQOppNLOiQ35Esj4YYLIYFAEkIaNwRCrwHTbeNe5V5kWb333tto\n6vl+jBkjLFmSJSMZzvNPR2e30WjOO3uvtd7NyGRKhoa68YtfSPDsK8+rT61vKDabmfauCvy8zwTX\nN7UVoFB7oJpBNSO/ju4Y44m7c1jNiKKIQq0b9R5DcAJypZaiys1kp38DQRAQRZGiys0oVDqC0ldj\nG+wi98DblNbuRKPypLO7Go+AGCIX3TalaxJFkfz1jyKTyQnwTSQieC6dPbXoNEZCAzP5eNeD+Hj6\njd3RaQSZnNjV3yN8wTr6WypQar3wCIydcEF0URQZaKvGOtCFh38UKr33RJcm8SVzUYq8i50dPY1k\nEeAWeABzBX+iRE929jZJIk9iWlF7+uEVksyp0o8J8ktGqzHicNg4WvAWMoVqxhuHRy65A1PiEtpK\ncnA6bPhEz8ErNOW8HT58o+ei9w1j99F/MCd5nSsmr/EoRZVbCF94CzLFzKrD93VyxxjLtWKwo47y\n7c/RVXUcEDGEJBG94pt4hSSdda9CrSNq2d2UbvsnXX0NBPok0NxZQltHKbGrv4dcpSVq6V34xmTR\nWrQLu8VMwqLr8U9cOuXvgd6GIvqaS4mPXElp9U5M3tGkxV/FkKWXw3mv4nDaCUyf+DmlSu+NT/Tc\n85qTuauJoo+eoK+5DHAJx6CMS4ld/V3JYWIGI4m8acAmiqg4+59ChRyb6JyGGUlIDCdu7Q/Ie+uX\nrN/6E/y8o+npb8Jq7Sfhiv+HUuM5dgfTjN4Ugd4UMSV9CTI5qTc/Ssknf2LP0X8AIJOrCJl7DREL\nb5mSMaaar4PQG8u1wtLfSe7rv0Aj15KdfjdymYLi6u3kvfUgs+96Cv0I5XRC5lyF2sufhiMfUNZ8\nEI13ECnLH8IvNtt9jyEkEUNI4oVYkhtzpyuLe17qbQiCjKMFb3G04E0ABGRErfwmGq8vFo24cDgd\ndk7959co7CIrs3+Ml2cwNY1HOZH7LnK1nuhld4/dicS0IIm8aWCBp4ktnY1cIUZgFFxHX1ViL6V0\nc63HxRcAK3F+DK14j6dmqL2S3hTB3G/+H02ntjLQUokpOpnA9DWTLn1wsaIxmMi47feYuxqxDnSh\n8wuf8WL3q2iDtuCFdIR5l/Cj/U1julY0ntiAaLNy2bLfoVEbAIgMnc+HOx6g7vB7JF4xctqTX2z2\nMFE3HWi8Xac5bZ3lZKffRUrsZbS0F9PcXkRF/X4CU1ZM2Vii6KStaA8tBbtwWM0YIzIInn0FKt2Z\nyhKdFUcwdzdx5fJH8fFyfXlKjbsCi7WfkuOfELnothm3oy3hQhJ508Cdplh29zbzkP0wWaI/Fhwc\noZVEjRdrvwY1+CTOkFlVjitXaOah1HkRnn3jdE9jGANt1Qy0VaM2mDCEJJ/3Eez5ovUOntYiyBPl\nq2SD9llRY9abgbHjIPsaSwjyS3ILPHBly4YFzqa2ocB9TXQ66Kw8hrmrEa1PCD5Rsyd1/NjXXEZb\nSQ6iw45PzDyM4ekTfp96habgYYomJ/d5stPuxNcYhSiK1DQdJSBlBUrd1JT2EkWR4k+eorVwJ/6+\nCXiqDTQcepeWvC3MuuOPqA2uuL/BrgaUSp1b4H1GgF8iBeUbsA52ozFMf/KRxNlIIm8aMCk1PB+z\nmNfbKzjY24ZCELjTGMs63yjUUmyDhMRZ2C2DFH30BJ2VZ2rd6f0iSLn+12i9pRjWczFZG7Q22xC5\nAx1oZHLmeZjQTMNnlFvgTQCl1kBvZ/lZ5XT6BlrcIsnc1cSp/zyEubsRqkO7YwAAIABJREFUmVyJ\n02FD5xNG2s2PTPg4VBRFKnc8T/3RD9BovJAJCuqPvI9f/EKSrv4FMvn4H7eCIJBy40MUffA4Ow6d\nsVn3i1/kKhsyRXTX5NJauJPFs79LdNgiAAbMnWzY8zDV+14l4fIfA6A1BmGzDdLVW4e3wV28gtaO\nUuRK7bBdP4mZhSTypgmTUsP9QSkgPZ8kJMakbMs/6KsrZMmcewkJyKCzp5oDJ18k/91HmPutZ2a0\nC8dM4Hxs0ERR5J8tJbzRXonzM1N7mZIHQzNYYvjy4sEm6lrxGYHpa8gr2s3JkvdJi7sSQZBTVrOb\nhpaTJFx2P6IoUvjB4yjsTq5Y9gi+xijauyrYffQZij58gsy7ngLA3NVIZ9VxZHIlvnHzRxU0nZXH\nqD/6AXNSbiUpZi0CAjWNR9hz7B805W4iZM5V55yvKIqITjsyuevYU2MwMevOP9HfWomltxWdTyhq\ng2lKa/K1lx7AQ+9PVOhC9zW91oe48KUUle4g4XQ1Wd/YLDReAew++g+yUu84nXx0hMKKTQTPvWba\nXF8kxkYSeRJnMeiw82l3PScGOtHJFaz1CmG2h++k+ux12NjV00Sfw0aqzpt0nfeXftQmcXFiHeyh\nrWgPc1NuIyp0PgCBfkksmvUtPt33W7pr8vCOnHVeffe1VNBdk4dcpcEUv3DKjsFmIhO1Qfu4q47X\n2iu4lihWEsoANv7jLOfXtcd4LW4ZoWr9BZ3v+bhWfB7vyFlELLqdvJzXKazYhCDIsdkGCUpfS0Da\nKvpbyulvrWDVgp/ia4wCwM87hqzU29l5+Gn6WqtoydtMw7GPEQQ5ouhE2PIMcZd8n6BZl541XmvB\nDry9wkmOudT92RYZkkV1w0Fa8rePKvIctiGq975Gc94W7JYB9KZIIhbdhilhEYIgoPHyp+n4BooK\n/4DTbsHDFE3E0jumJG7Qtct59hckQZDB55IAZXIlaTc/StEHj7PtwJOnb5IRmLaaqKV3TnoeEhcO\nSeRJDKPLbuG+yoPUWQeIw4tuLHzSVccdfjF8P/D8Msr29jbzcF0uVtGJGhlmHGTp/Xg8Yu60HP3M\nJA7ck8fOFxj1KMrc3URr4W4c1kG8wtLxiZ79ldm1EkWR7to8umtOIlOqMSUsHrHIsrWvHVF0DqtR\nB7h/HuppmfDYToed4k/+SFvxXuRyFU6nnYptzxJ/2Y8ImMKg9sngsJoR5MoJHfONh/HaoK3vqGE2\nJq4WXALIAyXfEVP4KTl80lXH987z82AsJuNa8UUiF9+Gf/Iy2ssOIDrs+MbMwyPA9b6x9ncB4O0Z\nOqyN0eD6ueXUNhqOfczclFuJj1qF3WHhROG7lG7+Ox6BsWf5xtot/eg0Z3951Wl9aBulcLEoihS8\n91t66wpJjFqNwSOQmsYjFH7wGElX/QxT4hJOvf1rLJ2NpMVcjl7nR2X9fgrWP0rKDb+etNDzjc2i\nKXcjdU3HCA92lVYxW3opr92Dzxf61vmEMPu//kZ/c/npOnnR7pg9iZmLJPIkhvFcSykdVgu/IYtg\nQY8oinxKLa+1V7DcEEjSBAu/dtiGeKjuBKmiL3eSgCdKcmnnuYECnmsp4YdByRdoJRcP5neOg+xs\nkddw/BPKtz2LQq5GpdRRd2g9xrA0Um98GLnq4vBnHA2n3UbBB7+js+IIGo0Ru91C9Z5XiV5xD2FZ\n1w+7V2MMRCZX0dSWj8nn88WI8wHQnUfGb93h9bSX7GdR5neICl2A1TbIkfzXKdnwFIbghGlNrugo\nP0z1nlfpb6tEJldhSlpKzMpvotQaxm48TsZjg9ZiM5PB8Ie4WpATIupptp3f7tpYuAXeFKLzCRkx\ngUjvHwkI1DYdJzF6tft6XdMxBEFGT20eoYGZJMe6rM8VchXZ6XfR0JpHc97ms0SeV2gqNTlvMGDu\nQK91nXzYbGZqm47iFTtyLf6e+gK6qk+wIut+woJmAxAbvpRdh/9K9d7XEOQq+prLuHTx/+DvGw9A\nTNgitu5/ktp9b0xY5H0xPtEneg5+cQvYdeRvhASko1EbqGs+DgolkUvuOKu9IAh4Bk3OIcPa30n9\n0Q/oqjyBTKnClLiE4MzLpSPfC4Qk8iSGsb2nkeWEECy4jmIEQWCtGM426tne2zRhkbelpxFRhHtI\nRCe4Yk1mY2KlGMrHXXXcF5iETDq2PYuB9lrKt/6ThKhVzEm5BblMSVNbATsP/4Wa/W8Qvfye6Z7i\npKg78h5dVcdZPu+/CQuag8NpI7d4PYU7/40xPG2Y1ZJCrSdo1lryTnyIIMgIDZxFR3c1xwrfxhCS\nhCF44jtKzbmbiQlbTEy4S1xr1J4snHUPDa0naT61jaild03ZWidCR8VR8tf/hkBTEhmZ32HA3Elh\n6afktVSQeffTE9rVG2ivpbVwJ/ahAbxCk/FLWOSO94KxbdCi1B7kmzu5XIxwC4M+0Uo1fSxXT21M\n3qzL7Ghvmj3h5IrJoDH4E5C6kqMFbzJk7SXAN4Hm9iIKyjcSmHYJnZVH8Q5KGdZGJpNj9AzG0t95\nVn9Bsy6l6cRGNu79DYmRq5DLVZTW7MLqGCJslCz13vpClEodoYGZ7muCIBAdtpC6I3+jqyYXnc7P\nLfBcv5cRFbqAA7n/xmm3jVm6xGm3UbP/LZpPfop1sBsP/xjCF647fRwsI/naX9KUt4XWgp30DDXg\nn7GG0LnXjrhL57TbGOysR6HRozH4n3PckbD0tXPi1Z/gHBokPGgONpuZyp0v0FF+mLSbfzPlu9YS\nksiT+AJW0Yn2C28LmSCgFuVYnI4J99dpt2AUVOgY/kEUhJ4Bpx2b6EQtfL2PbEeitXAnKpWeuSm3\nIj/9YA72TyU+YhkVp7Zf9CKvJW8b0aEL3UdECrmK2cnrqG44REv+9rP8NKNXfBPR6ST35BnvTZ/o\neSRc8ePziu20DnZhDBu+WyeXq/DQ+WMd6DrPVU2e2pw38PeN55IFP3cfywf7p7Jxz8O0lx7AP2nJ\nuPppOPYR5dv+hU5QYhDUFJ3YgOfBd0m79XGU2jP1/c5lg3abKYYHao/yPEWsEEMYwMaHVKGWybnK\nZ+rqJWp2Xs/lfwyE9VPW5biJX/tDFGoP8k9uIq/kA+RKDcFzriJq2d0UfvA4da25ZCTdgOz032LI\n0kdrZykh8WeLNqXWQMYdT1K16yVOln6I6HTgHT2HjKW/Qucbetb9AAqNB3a7BYu1b1iplwFzB4JM\njkpvxGLtw2YfGuap2z/YhlypQZCP/dlZ9PGTdJYfIS5iGUbPUGqbj1L4wWMkXvlTV2iCIENvisQ7\nMtMdNjGSwKs/+hG1+9/EZu4FwBiWRvzlP0JrHH/2YO2B/4DVyjUrHkOn9QGgqa2Qrft/T3tJDv7J\ny8bdl8T4kESexDDm6f3Y19/ESjHULb4KxE6aGSTLY+J1kJK0Rt4QK6mhz23jJooiR2klSuUhlYwZ\nBZu5H63G6BZ4n6HX+WEb6h+l1cWDfagPz8Dh7yeZIEOv9cVm7jvrfplcSdyae4lcfDuDnQ2Iokhv\nYxGNxzfgHZkx4Zp5ngGx1DQfc2VBnn6A9w200tVTQ2zgZePqQxRFBttrcNgsePhHTfq4SXQ66G0q\nISn9G8PiLv28o/H0CKS3oWhcIm+ws4Hybf9iNSHcLMaiQEYVvfyxPY+qPS8Tv/YHw+4fzR1jiSGA\nB4LT+GdLCQcczQDEqD15OjQbH8XU+BeP5VpxoZEplMSu/g5RS+/COtCFSu/tDoUIy76Rk2/8gh0H\n/0RC1GpsdjP5ZRsQFCqCM85OvADX7mDS1T9HFEVg5KSGz2NKWETFjuc4lPcKC2bdg0qpo6O7ivyy\nDfjFLyIw7RJq97/FobxXyEq7E6VCQ0tHMcVVWwlIXTVm/33NZbSX7mfxnO8RfTqDNj5yBbuP/J3q\nPa/gF7+Q4g1/or0kB5XKA4fDSuWuF4ld9W1C5lzt7qc5bysV258lLmIF0WELGTB3kFv8Hnlv/oq5\n3/qn2+96LDrLDxMTutAt8ACCTMn4ekfTUXFYEnkXAEnkSQzj2wEJfH9gPw+Jh8gSA+jGwmFamaP3\nZaHnxLfnl3gGEKX24GnLSS4XI/BFwwGayaODRwIyx+7ga4pXaBJNuRvp6K7G1xgJgNPpoKrhIF6h\nF38co2dIIlWNh0mJuxK5zPUx1NvfQntXBTFzVo/aTqnzoq9wFxU7nkcuUyKXK6nJeR2/uAUkXfPA\nuI97wheu49Q7D7Pj0J+Ji1jOkKWXU2WfoPb0wz957MSLvqYySjb+mYH2Gte8NJ5ELr2L4MzLxzX+\niAgyFGoP+gdbh1222y2Yh3rw1Z07Jk8URYa6m2k49hEaQclNYgyK0yIgSjCwSgzm0/wdxK257yxB\n/Hl3DLvoZMBhx0Ou5CqfcNYaQ6iw9KEV5ESoPSadFT8R14ovC7lKg1Y1fEfKKzSZlOt/TeWO59l5\nuladISSJ9DW/QOXhM1I3blyv0divk1LnReKVP6Xooyep35yLVmOkf6AVvV8Esau/g0rvTcLl91Oy\n8WlqGo+gUukxmzsxBCcSNQ4rsZ66fGRyJZEh84fNLTZ8MbWHjlCz/006Sg+yZM73iQzJxu6wcqLo\nXYq3PYshNAXP00kqdQffITx4Hgtm/Ze7H1+vKD7c8QBtxXsJTBv9f/YLLwzOEU6EnE4Hcils54Ig\niTyJYcRpDfwrZhEvt5VxsL8ZnUzBN7xjudUv+rz+CZUyGX+Nms+fGwt4u7ccJyJBSi2/9s9gtdfF\n4xxwIcndpGDjZX8dFnBuSlhC3cH1bD3wJEnRa9Cqvaio20dndzXpl/5uGmc7NUQsvIXc13/B5n2/\nIy5iGRbrAEWVm11xUikrR23X11xOxfZ/kRS9lllJN6KQK6luPMy+Y89Sf+R9wuffNK7xfaLnknzt\nL6na9RK7Dv8FEPCJnkPsJd9Hodads611oIu8t/8Hg9bE/Pk/Qa3ypKR6O2Vb/oFS54UpYdFEXgo3\ngiAQkLaaktxNBJlSCDKlYrcPcTj/NRwOC/7Jy0dt29tQTPmnf6OvvRoAb9QovxAG4Y0Ku93iKo0x\nQojE/b/3JXKZnfX/t4NBpw0fhYbb/aJY5xtFknZqxNhEXSumG9/YLHxi5jHU04JMrkTtOblSUiNh\nSliE4Xsv0Fq4C+tgN2GB8fjFz3fHTwakrMQYlk5r0W5sQ/14hSbhEzVnXK4ccrUep9OOxdKLVnPm\n9R4wdwEC7cU5RIXOJyp0AQBKhYa5KbdS03iEllPb8AyIwemwMdjVQGjE8B1uL88g9Do/yrY8Q0fF\nYcIXrHOLwtHwjV9IxcktJEZfgsHDtYNb23SMrp4aklfcPpGXTWKcSCJP4iyiNZ48EjZ7yvrzUah5\nNHw2Aw47g047vgq1lGzxBXI3KXhqZ7P76EqmUJJx62NU7nqRU0Wf4LRbMQQlkHbzoxjD06d5tpPH\nEJxI+rrfUrXnZQ7kvoAgU2BKWET0invOKbJa8reh1XozJ/VWd5xUVMh8GlryaM7bOm6RB66Hq1/8\nAqx9nchU6nF70TbnbUW0W1k9/6do1K42vsYoBswd1B9+77xEniiKNOVupKviCA6Hje0H/4RCocHp\ntOMUHcRf9iO0xpGPNYd6Wjj19oOE2tXcTRp19PEh1RSLXSQK3gA4RCf7hBaMwcmjioPiDU+RU3qA\npOg1+HlH09CSx99qdzPkdPAN/8llVML5uVbMBARBGPW1nyrUnr6EZd8w+u8Nfuf8/Wj4xS+gfOs/\nOZT3Kosyv4VSqaW7r4FTZR/jGzOP3oZiDIHDk2hkMjkeehO2wR4ABJkClc5IR081sSx13zdk6cNs\n7iTAL5H+hgpyX/0p6bc+hldI0qjzCV9wM10VR/ho54ME+6dhtQ3S2lGMX9wC/OLmj9pO4vyRRJ7E\nl4ZerkAvZU+NG6XOi4TL7yf+0h8iOp3TagAuiiJd1SdoLTxtYh6eTkDqqjF3vc6FMTyNzDv+iMNm\nQZDJx3XUajP34aE1uQXeZxj0/tS1nZzwHARBNuFaXwMdtfgYI90Cz9WPQLApjbzyjyc8B4Cq3S9R\nd+hdIkKy8Q9ZTmN7AQ3NufjEzCN+zQ/OOceG4xtQ2p38TMxAKyiYJfpRQCdPc5IVYgg+aNgvtFJL\nH2kjlMUAGOyoo614DwtmfZO4CFdcVETwPFRKLa9Xb2edXxRa2fn/756va4XE5FBqPEm88icUffQk\n72z5ETqtD719jWiNQcSuuZeyzX+npukoqXFXIjv99+0fbKO9s4LoWa6wBUEQCMq8nNIDb+NtCCMm\nbBED5g4OnnwFmUzJkjnfR6nQsGnfb6ne8woZtz4+6nxUOi8y73qKxpOf0lV5DJnOk8T5P8E/edmk\n/IIlRkd64kpIzHAEmXxaPwBFUaRi+7M0HPsYL88QNGoDFWX/ovH4BjJuf2LSvpXjDdoGMAQnUFG0\nl76BVjz1rhhRp9NOddMRPM+jlMp4sPZ30lWbh0ymwDtqNhpDAE2lh7DZLSg/l4DQ1lU+Yb9TcB3/\n1h/5gIzE68lIuBaApJi1HMp7hYq6/Sg0HudsP9BaSaJoQCu4Ps5lgsCPxVk8yQm20YBTAK+QZNKX\n3IExPG3EPnqbSgGGxW599nNhxafUWAZI1E787/z2s7cB8CtJ4H1pDPW20t9SiUpvxDMowXUc/N1/\n01ywHdtAF0EB6zAlLkGuVBO+YB0n33yALfufIC5iOVZrPwWVn6L29CUwbZW7z/AF6xjqbuLgyRc5\nePJFANQqT1Zk3+/OCo4LX86hvJdwOmzDSvV8EYXGg/DsG0esXSgx9UgiT0JC4pz0NhTScOxj5qXe\nQWL0JQiCQE9fI5v2/Zaafa8Tt+beL20uAamrqT/8PptzHiM55lLUSj2lNbvo6Wsi46ofTelYoihS\nk/MmtQfeQjwdLC5XaolYeicOp409R//OnJRbUKs8Ka3eQV3TMeLX/nDC4/TUFyI67cSFD88sjItY\nTknVNvpbK/AKTRmxrdNhR3Q6qGUApyi6wyC0ggI1CgwhyaTf/sSYc1CeFnB9Ay34eJ0pj9I74Mqq\nNZ7joT0S7qLGH02omcQkcDpslH76d1ryt8Npr2G9XyTJ1/4SnW8oEQvWndXGKzSZtJt+Q9WuF8k5\n/iyCIMM3bj4xK7+N4nO2dTK5gsQrf0r4gluo3PUi3VXHuW71H1Epte57bPZBBJniK+PI81VBEnkS\nEhLnpK14LzqdL4nRq92ZlV6ewcSFL6O0eO+XKvIUah0Ztz9BxY7nOVbwFqLoxDMwnrSbf3POWKDz\noa14LzU5r5MWfzVJ0WuxOywcL3qHqh3PE7Pqu9TkvM5HO34JuHZbw+bfRGDG2gmPI1e5jryHrL3o\ntN7u60OWntO/147YzjbYQ95b/0N/WyUAr1DC9WI0SmRsppZSukia/d1xzcE7chZqDz8O5r3E0jn3\n4qHzo7OnhhNF6zGGp3PpNSpyN41vPRN1rfiiC4PE+VG1+2XaCneRlXYH4UFz6elv5NCpVzn1zkPM\n+/a/Rg2H8I6chfc3/oJ9qB9BrkCuHN1NR+cbStSyuzlafpDC8o1kJF6HIMjoG2ijqHKrq8CydOw6\no5BEnoTEDCHxyf/AFFs6TQVOuw2lXHPWN3SlQovTbvvS56Mx+JNy7a9w2q04HfZJxQWei8bjnxBo\nSiEz6bNjJU8WZ36blo5iBjtqmX/vy3RV5+KwDeEVlop6jLIao2EMT0Ol9+Fo/pssz/ohKqUes6WX\n40XvovMNQ2+KGrFdxY7nsPW2cfnSh+nsqSEn71X2iC6PVEGQEb7gFkyJ40t0kMkVJF/3S/LffYT3\ntv0EjdqLoaFudN4hJFzxYy6X+Y9pgzYR1wrbUB/Ve1+jtWAndqsZY1gqkUvuGHXHciZgM/fRUrAD\nc1cjOp9QAlJWjHiU3tdcRm9DMQqtJ36x2aOK9KnEYbPQlLuJlNjLSYy+BACd1ptlc+/j450P0lF+\naMyEoLHCAj5D7xdO5JI7ydv7KpUNB9BrfWntKEVt8Lvoi7R/FZFEnoTEDCF3k4KdL+ybcRmIPtFz\naDr5KU1tBQSZXA9hq81Med0efKJH9uT8MpApVJMuQGwd6KanvgCZQo13RPqw/iw9bYQFZg0fU6bA\nxxCOubcVmUKFb2zWF7ucMDK5gsSrfkrB+t/wzpb78fIIpru3HrlKQ9q6R0fc5XLarbQW7SUz8Qb8\nvKPx844mPHguNQ1HOHzqFSIW3UrEotsmNA9DcCLZ33uBtuJ9DPW6arX5xs137wCdywZtIq4VToeN\nvLcexNLZRGLkKld5oPocTr75KzJufXxG1oHsbSzm1H8ewmmz4OkRSFPfJmpy3iBt3W/dZUMcNgtF\nHz1BR/khBJkc0elAofYg+doH8I68sDVBbeYeHLYh/H3ih133NoShUGgZ6m6esrFEUcQrNIXA9DUM\ndtRh03gQnfFNAtNWDzvilZgZSCJPQkLinPjGZmMMT2f7wT8RGZKNRm2guuEwVscQiYsvztpWoihS\nve816g6+i+i0A664tIQrfoxvzDwAdKYIGtvyyRRvdO9i2mxmWjvLCIycRNHjEfCOyGDed56j+dRW\nhrqbifRdSWDqKpSjJLU4bBZEpx299kzdNo3Kk/jIFeSWrD8dkTVx5CotgemXjPr7kWzQJupa0V6S\nQ39LBZcvfRg/72gA4qNWsnHPI9Tse530W2ZWHUjR6aDowycx6oNYMe9HaDVeDJq72HH4aYo/+gNz\nv/V/CIJA9d5X6a46wZK59xIRnMWguYODJ1+i4L3fkv39F1Fqz13MejKodEYUKh3N7UWEBJwpsdTR\nXY3dbkbrEzIl4zhsQxS89zu6qo+jUnlgtw8hAv5JyySBN0ORRJ6EhMQ5EWRyUm98mPqjH9BSsAtH\ntxmvmFmEz795VE/OmU7LqW3U7n+LtPirSYhajdXaz7HCtyl8/3fM/dY/0RoDCc26jry3HmT3kX+Q\nFH0JdoeFk6Uf4sRJcOYVUz4ntYfPiMHxI6HQeKDzCaOyPofIkGz3bl9j6ykslj40Bn96G4rR+YVP\n+XH2523Q3n72tgm7VnTXFeBlCHULPAC5TEFM6EKOF787ZfMUnQ4ajm+g5dRW7OZ+DKFJhC24GQ9T\n5IT66WkoYqi3hRVLvoNW4xLdOq03c5JvZuv+J+hvKcfDP5rmk5tJjLqEqNMZyh46E4tmf5d3t/yI\n1qI9hMy+csrW9kVkChVBs6+k8NB61Cq9Kyavr5EjBW+g8w5xf3GZLDU5b9Bbl8+KrPsJDczEahvk\nyKnXKNn4Z7xCk88ru1ziwiKJPAkJiTGRK9VELFg3bhEy02k49jGhgbPd8XY6jZGlc3/Au1t+RHPe\nZqKW3o13RAZJV/2Myp3/pjbnMQD0vmGk3fwoGq+JW/xNJYIgELnkDgo/fJxtB/5AZEg2fQMtFFVu\nRaHWU7LRZcMlV2oInXcdEYtvn9LkhuUPmMl49jZOfjRx1wqFWseQpRen0+6uzQYwONSFQjU1glQU\nRQo/eoKO0gOEB83FIyCRmtqjnCj7f2Tc9nsMQfFjd3Iau2UAAK3Ge9h1ncYVg2kfGsBhs2C3DuJt\nCBt2j1ZtQKsxYu3vmOSKxiZqyR04rIOcyH2P44X/AcAQlEDy1T+fsmSI5rytxEeuICzIVSxfrdKT\nnfENapuP01Kwk4iFt0zJOBJThyTyJCQkvnZYelsxRQ13dVEq1HgbwhjqOeMd65+8DFPiYgbaqhHk\nSnS+YTMmE9SUuJgU2YPU7HuTA7n/dmXpyuWo5VoWzfsvPPUBVNUfpGD/m8jVOsKyrp/S8c9H4AEE\npKyg7tC7nCh6l1lJNyKXKWjpKKG0ZheBmZeN3cE46KkvoL0khyVz7iUq1LWzlpFwHZv2PUr17pcn\ndCRsCEpAkCmoqNvnrmMIUF67B7lCjWdgLHKVFq0xiLrm40SHLXTf09FdzeBgBx7+57b7mgoEmZy4\nS75PxMJbGWirRqX3Rm+KmLL+RVHEZu7FoB9+NK9UqNFpvd0OGRIzC0nkSUjMIA7ck0fGs6nn/QCV\nGB9an1Ca2gpIjbvSLdos1n46uqsJSx6eTCHI5HiM4ck5XfjFL8QvfiFOh4224hyKP/kDKxf9D94G\n1zG6j1cEFls/dYffJ3TetTOihpneFEn0im9SsPPflNXuQa3yoK+/GUNw4oSTRUajs/IYGo2RyJBs\n9zWFQk18xAoO5b08ZsHez6PSG/GJnsvJ4vfo7W8iwDeRpvZCahoOEbHwVndWatiCmynd9Bdyjj9H\nVOgC+gfbOFn6ATrfMHzHYdk1VaVkVHojKv2sSffzRQRBwDMwnurGw8RHrnC/lzp7auntayR4Aruj\nEl8eksiTkJhhrPvuG3CeR2ES4yMs+3oK3v8dB3JfICFqJRbrALnF6xHkcgLT10z39CaMTK7E3FmP\nVuvtFnifEWxKo7xmN3bLwKj+vObuZppyNzHYUYfGGETwrEvR+YaNeO9UEJZ1PT7Rc0/b5A0SHp6O\nb2z2lB0ryuQKnE4bTtGBXDjzmLPZh0CQYxvsRaU3jms8c3czHRVH8DaE09pRRlX9ATRqL0BA9rma\nckHpaxAdNmpy3qSibi8g4BubRdya+0atUeewDlG97zVa8rdjM/dhCHEJXZ+oqfMOn0oiFt1K/vpH\n2H7wKWLCFzNo7qKgfCM63zBMCTOrKoCEC0nkSUhIfO3wi19I3Jp7qd7zKuW1uwHQ+YSRtu7R8653\nN92ovfwxD3XTP9iOh+6M1217VwUKtceo8W5dNScpeOdh1E6IFj2pluXSdOxjkq771QU1jdf7hRO1\n9K4L0rcpYRE1OW+QX/YJ6fHXuF1a8ko+QBAEDj5zFyq9N6FZN5ze4Rx9B6351FaUCjWXLfk1CoXa\nveO27/izNJ38lPD5Z+y5gjOvIDB9LUM9LSg1HqNmRwOIopP8dx+mr6mUhIgVeOj8qWo4wKl3/pfU\nG/4X35i5k34deptK6Sg7gCiK+MZkYQhJmtRuoW9sFinXPUj1nlfp8XsiAAAgAElEQVTYe/QZBJkC\nU8IiYlZ9e1q9tSVGRxJ5EhISX0uCM68gIHU1/S0VyFUa9KaocT0AbeY+6g6/R2fZQUDEJ24+YVnX\nX9ASGV+kr6mM5lNbsA72YAhKIDB9Df6JS6ja9RK7j/6d7LS78NQHUN1wkKKqLYRm3TDirpXodFC2\n4c/EOPX8WExHLcixOR08QwFFG57G5wevXpQPb70pkoiFt3Jy/5tU1bsK9ja3FyIIMlJjL8fXK5L6\n1pOU73wep8N6zoQia18HnvoAFKd9ij97j/h4RVDddOSs+2VyBbpxlCzpqjpBd90pVi/4GcH+Lk/h\n+KiVbN3/BNV7X5mUyBNFkfKt/0fjiQ2o1QYEQUbdwXcISF1FwuX3T+rY3i9+Ib5xC7AP9SFTqCfk\nPS3x5SOJPAkJia8tcqV6QsV37UP95L72M6y97USGuGL3qo9+QkfpAWbd+cdRj0OnCttgD0Uf/4Gu\n6hNoNd54eQRSXfYqDUc+IOP2J0m76REKP3iMjXsePt1CICB1JZGLR45162upwNzXxnVkohZcIlAp\nyLlBjOYhy2G6a/OQq7Su48ShPrxCkglMv+SiqIkWueQOjBHpNJ/azmBPC6LoZFHmt4kOczk/hAfP\nRSlXU3ZoPaFzrxnVzssjIJqK/B3DdkhFUaS26fikEip66gvQaIwEmVLd12SCjJiwxew/8RwO29A5\nLcbORXvpfhpPbCAr7U7io1YhABW1+9if+zzG8DQC00avhTgeBEH4Ur/USJw/ksiTkJCYEYhOB33N\n5SCKeATGjhrHNJ00HP+EoZ5mrl7+WwweQQCkxl7Bx7v+h8bjGy5oCQnbYA/HXr4fS28rSdFrmJN6\nGzJBxqC5k09zHqN827Ok3fQwWd99nu7aPGyDvXgGxaP1Dhq1T9HhsqXTfOFRoMEl+FoKttNauBtf\nmQ5fUU15yX4aj3xIxh1/QG3wO6u/sRBFJ901efS3VqEx+OEbmz2ma4nodNB08lOaT27BNtiLR3Ac\n4dk34RkUN+Z4xvB0jOHpNOVtoafuFBGfS8QAiAjJpqhyC+auJjz8R7aPC0hdRd2Bd9h64Eky4q9B\nozZQWrOL1o5iUm54aPyL/wJytQ6bzYzdYUGpOCPmBs1dyOQqBNn5v/9b8nfg5x3rtjgDiI1YSlXj\nQVryd0xa5ElcPMy8T1EJCQnujh/i/033JL5EOioOU7b5H1j62gFQ6b2JXf1dTIlLpnlmw+msPEpo\nwCy3wAMweAQSGphJZ8WRCyry6o68j62/C7lMyaykG5GdPnLTaX1IibmMQ3mvYLcMolDrxm2j5REQ\ni1KtZ5uljnvEM/Fa26hHJlPQWribq4jkGmcUMkGgFTOP95+gctcLJF398wnN3zrYQ/47/0tfcxly\nuRqHw4JK70PqjQ/hGTi6YCv99K80n9pOWNBsDAEJ1DUc58RrPyV93aMYw9NHbfd5PouN6+1vHpaY\n0tvvsvs6166UQq0n/bbfU7b57+w7/iwAGk8TiVf+FL/Y7FHbjYV/0lKqd7/M0fw3mJd2Bwq5io7u\naoqqNmNKWjqpLzn2oT6M2rNjS/VaX/oGa8+7X4mLD0nkSUjMQIZWvMdTE7SLuljpb62i4L3fEeSX\nTHrG9xAEGQXlGyn86EkyPU0YQhKne4puZDIFDqv1rOsOhxVBcWE/TjvLDuPtGUrvQBNy2fA4OaVS\nC4hui7bxIleqiVrxTXI+/StNwhDJohdlQh8ldOIdnslQTQFXiZHITos/f0HLajGY90tyEJ2OCWXD\nlm76K7auVtYsfIAAvyT6BprZe/xZCtb/lqzv/XtEUdPXUkHzqW3Mz/gv4iNXADAr6Qa25DxO5c4X\nmH330+Ma2ydqNiq9DwdPvsiSOd/HQ+dHe1clJ4rfxSdqDmpP33O21/mEkHHr41j6O3FYzWiNgZPO\nBNYY/Ilb+wNKN/+d6sbDaDVGevsa0ZsiiVlxz6T69gpLofHox5gtvWjVLgFrtQ1Q13wC35Slk+pb\n4uJi+osmSUhIfK1pPPYxWrUXK7Lvx983HpNPLEvn/QCDRyD1Rz+Y7ukNwy9xMY2tp2huL3Zfa2kv\npqHlJKbE8yshMdTTSn9rJU772eLx8wgyGRqNAZt9iMq6HPd1p9NOSdV2PAJiUJxHTGBQxlpSb3yY\n7rAItut6aQkOJPmaX+IRGItSkCNneDKKDgVOpx1RdI57DOtAFx3lh5iVeD2BpmQEQcDgEcTCWd/E\n0t9OZ9WxEdt1VR1DodASG35GmMhlChIiV9HXXIbN3Duu8WVyJSnXPUj3YDPvbf0Jb396Hxv3PIxM\n50n8Zf897nWoPXzQ+YRMWamXoIy1ZH37XwRnXYtH3GySrvoZs+9++pxZueMhePZVCEo1G/c84vrC\nVPEpG/Y8ggMnofOum5K5S1wcSDt5EhIS08pgRx0BvgnIPxeDJBNkBPom0thRNY0zO5ug9LW0l+Sw\nJecx/H0TAIHWjhKM4WkEZaydUF/m7iZKNjxNT30+AEqNJ+ELbyFk7jUjZvn6xi+k/uA7hATM4kDu\nv2lozcPLI4jqxsP0DbSQdvNvzrs8hm/MvLP8TRVaT+oOvsNx2piDy8bNJjrYJTTjHZo27mLC4Dqq\nBRGj5/CsUy+PYNfv+ztHbCeTK3GKdhxOO7LPCSu7YwgQJhS3ZghJJPv7L9JWkoOlrx29KQLfmKwp\nE2zni9Y7iMjFt09pn2oPHzJuf5Kq3S9xvOgdVwmV6LkkLPsGWuNX/3RA4gySyJOQkJhWNMZA2msK\ncYpOd5yZKIq0dVWg9h89aWAkbOZeWvK3Y+5qQusdREDqqinNApQplKTd/BtaC3fRXnYQREjI+hH+\nKcsnJHoctiHy3vwVCgcsmXMvep0vlXU5lO54DrlaR9AIBZlD511LR+kBl7jzDKKx9RS1TcdQaD1I\nv/X3GMNSpmyd4Epa8IvJ4v8qjjJPbMeEmsNCOx0yK+nLvjGhvrTGQBQqHbVNx/D3PeOMUNd8HADP\nwNgR2/nFL6Ji5wucLH6POSnrEAQZ5qFu8ss3YYzMRK7STmgecpWWwLTVE2pzsaLzCSHlugcRnQ6A\naRezEtODJPIkJCSmleDZV3CiYCf7jz9HesI1CIKc/LJP6OqpIf3S74y7n97GYk795yGcNgsGz2Ca\n+zZTk/MWaesenZAh/VjI5EoC0y6ZVIZiW/E+hnrbuGbl7/HydAlZf584LNZ+6g++O6LIU6h1zLrj\nSZryttBRfhhPeRB+8YsISF05IYE5XgRBIOm6X9Fw9CMK8rZgH+rBM2wWsxasm7DNm1ypIWTedRTm\nvIHTaSc0cBYd3dWcKvsE78jZoyZeaLz8iV7+XxTu/De1zccw6ANobi8CQYazppV9T92AKXEJ0cvu\nRnWRFrG+0Eji7uuNJPIkJGYos/2iAPN0T+OCYwhOJOGKH1Ox9Z9U1rtizeRKDXFr7sU7cnwenKLo\npPijP2DUBbEi+360agPmoR52HP4zxR/9gXnfeXZKfFstfe00HPuY3oZilFpPAtIvcR35TfCYdKCt\nGg8Pf7fA+4yQgAxqThzGabeNWIRYrtISOvcaQudeM6l1jBeZXElY9g2EZd8w6b4iFt2CIJdTcfh9\niqu2IpMr8U9eQcyqb5+zXVjW9RhCkmjO28pAVyMABn0AceFLsdrNFJdtI7e+kNnf+AsK9ciuHhIS\nX1ckkSchMUPZn/Yndp36Ccsf+OoLvcDUVZjiF9JVmweiE2N4xoQe2L0NxZh7mlm6+EF3NqFW48Wc\n5FvYkvMYfY2lk87SHWiv5eQbvwC7nWBTKn1tzRSs/w2hWdcTs+KbE+pLbTAxONjBkKUXjfrMcXJH\ndxUqnRfCDKwROFkEQUbEgnWEzbseS38HSq1h3H9jr5AkvEKSKPzw9+i0vly+9H9RyF319aJCsvlw\nxy9pyd9OyJyrpmSuoijSVXWctpIcRKcdn6jZ+CUsnpG1GyUkzoX0jpWQmMGIR7YCXw/jb7lKe951\nx+yWQQB0Gu9h1z/72W4dnNzkgMqdL6CR67hs+a/RqFxZrPllGzh++G0CU1ehN0WOu6+A5OVU73mV\nPceeITvtLvQ6P1dMXs1Owhesm5S/6ExHplCed/B/T10+CSFL3AIPwOARhMknjp76AvziF9CYu4n+\nlkrUnr4Epa8dV9HkzyOKTko2/oWW/G0YPINRyNUU5W/H68Qm0m5+5LxdKCQkpgNJ5ElISFz0GILj\nkcmVlNfuITPpjGF8ee0eZHLVOYvtjgen3Upn5VGy0u5wCzyA5Ji1nCr7mPbSA2eJPNtQHx1lB3HY\nLBjD09H7hbt/p9R5kXLDryn68Ak+3PGA+3pAykrCz+Gj+nVHofZgwDw8E1cUnQwOdaF2+nL03/eB\nw0GAbzzdTUdoyt1E3NofEDzrsnGP0VF+iJb8bSzM/DYxYYsRBIGW9mK2HniShqMfEb7g5qleloTE\nBUMSeRISEhc9Sq2B0KwbOHXgLfoGWgnwTaC5o5iahkNELLoNpXZynrKiKAIigvDFIHYBQZBhHeyh\ndPM/6G8sQaE3ovMJpenkpzjtFgRBjig6CEy7hPhLf+gOhPeOyGD+vS/RWXkM+1A/hpAkdL6hZ419\nLmzmXtpLD+CwWfCOyEBvipjUOr8M+lsraS3cjcM2hHfELHxjx1/GJCB1JdX7XicyOIuQgAxE0UF+\n2Sf0D7Ti7FDjqfFhzcIHUKs8cIpODp18mYptz+IXvxDVOGvPtRbuwccYRWz4GbeVAL9EIoKzaCva\nLYk8iYsKSeRJSMxgzO8cB9nX47h2skQuuQO1pw8NRz6kuvEwOmMQcWvuI2gCuzijIVeq8Y6YRXH1\nNqJDF5x2mICymt1Yrf00521GpdAR6p9BZ1ctDVXHiAxZwLzUW1Ep9ZTX7uXQqZfR+UUQlnWmGK1M\nocIvfsF5zamlYAelm/6G02lDJsipcNoJSF1NwmX/PeGMyqHeNtqK9mAb6sMrJBmf6DkXJCuzZv9b\nVO99FbXagEqpo/H4J3iFppJ20yPIVWMfg4bOu46eunx2HHoKvc6E3WHBYuklZM41NBz7kLlz70Ot\n8gBctRYzk26krGYXneWHCUwfXza00zaESnl2rKBa5YGj3zKxBZ+D3oZiWov3IjqseEfOnpDYvVjp\nqS+krXgfTocNn6hMfGOzv/Jrnm4kkSchMYPJ3aRg42V/5XLZ+Kvyf10RBIHgzCsIzrzigvQfteIe\n8t54gPd3/IKwgEz6B9toastHqTPiofTi0sX/g1Kh5njhO/QPtLIo85vIT8eOJUStpLWzlObcTcNE\n3vky2FFHyYY/ExW6gLkpt6JU6qio3cvBvJfQmyInNEZL/g5KNj2NTFCgUumpO/gOhuAk0m5+BIVa\nP+m5fkZvUynVe18lPf4a0hOuQSZT0NxWyPZDT1F78D9ELb1rzD5kCiWpNz1MV9UJOquOI1eqMCUu\nQa7U0nDsQxRy9bD7FXIVgiDgdNjGPU9jZCaVO56nu7cBo8FVvHnI0kd1wyG8kxZNbNEjIIoilTtf\noP7Ie2i13igVWhpPbMQYnkHqjf+LXKkeu5OLDFEUqdj+HA3HPkSn9UWhUNOUuxFjeDqpNz78lVzz\nTEESeRISM5zcTQoynu3m5EfG6Z7K1xrPgBgy736a+iMf0FRfiEJnIHbNvZRveYbkzJtQKlwPqsGh\nTgwegW6B9xnehjBqW06M2LcoOumuyWOwsx6tMQjvyFnn3OFoPrUVlUrPgox7kJ+ukRcfuYKWjhKa\nT346bpE31NtKyaaniQ5ZSFbaHSiVWprbi9h5+Gmq9rxC3CXfH1c/46G1YCc6rQ/pide5i14HmpKJ\nCVtMbf6OcYk8cGXp+kTPwSd6jvuaKDrReYdQXLWVYP80tztGcdU2RFHEOypz3PMMSr+E5txNbNr3\nKLFhi5HL1VTU78Mhg/D5N47dwRh01+ZRf+Q95qTcQlLMpcgEGY2t+ew49Gfqj7xPxMJbJj3GTKOr\nOte105p6G0nRaxDca37qK7vmmYIk8iQkJCTGic4nhPi197l/tg/1U77lGURRpH+wHYu1Hy/PUKrr\nDzJg7kSvdRXoFUWRuuYTIxYRtvS2k//uw/S3VYEggCii8wkj9aaHR81CtfZ34akPcAs8q22Q7r4G\ntGoD1taRLcJGorVwF3KZgqz0O1EqXMelgX5JJEZdQuGpLcSu/u6U1BcEsFsG0Ki93ALvM3Qan0ln\nPwuCjOiV36LgvUf5cOev0GmM9A+2MTDYTsicq9Eax++cIldpybj9CWr2v01lyT6cNisqgx/efhF0\n1ZzEP2nppDJsWwt2YvAIIjnmMncWdbB/KpEh2TQX7PpKCp7Wwl0YPINJil77hTXPp6Xwq7nmmYIk\n8iTOQhRF8ga7aLYNEqn2JEE7ObNsCYkvE1F0gih+KbE+Co0HnkHxHMl/HZvdVc9QIdcgCHI273uM\njIRr0ag9Ka3eRVtnKWmrHjmrj6KPn8TZ38vaRb/C3zeB9q4K9h7/J4XvP8bsb/xlxHIqHgExVBbt\npn+wjfKaPRRWbMLusLrGV+vdtm5jYTP3oVYZ3ALP3b/OhMM2hOh0IMjHL/LMXU3U5LxOZ8VRBJkc\nv4SFRCy6DZXeG2NYGiX5O+jsqcXHy5Vp7HBYqWzYj1dY6rjHGA3f2Cwil95N9Z6XGRhsc5fP6aw4\nylDW9WgMpnH3pdQaiF31bbxCkyn66EnEzkYUQ1ZKC3dRt/9t0m97HI3B/7zmabcOolF7nfV31aq9\ncHRNvtTPTMRhNaMdZc32rq9+HdDpRBJ5EsNotg7yi5qjlFv63Ndm63z5bfhsvBSqc7SUkJhehnrb\nqNr1Iu2l+3E6HfhEZhK17O4JW3BNBKfDhm2gG6VCQ3bG3XjqAqhuPEhRxWaGnGZyTvwLAI0hgKSr\nf4FP9Nxh7Qfaa+mpL2DZvB8S4Ocq1mzyiSU77S62H/wj/c3lI9Z5C0hbRf2h9WzY/TAWax+pcVcS\nFTKfvoFWjhW+Td7bDzLvW8+O6JrxebxCkqg//B6tnWX4+7jGEUUnVfUH8AyInZBd2lBvK7mv/QSF\nKCcpfCUOp52ygt10VZ1g9t1PY0paSv2RD9iy/3ESIlaiVntSXruX/sF2Zi38+bjHGQ27ZZDa/W8R\nEpDB4tnfQaXU091bz7aDf6Js8z9Iu+lh1/qcDvrbqkEU8fCPGvXLgG2oj+JP/kR44GwWZn4LpUJD\nT18TWw8+SdnmZ9z9TRRjeDoVpc/S09eIl2ewayz7ENWNh/GKTD+vPmc6xvA0Ksr+RXdfA0ZPV5yj\n1WamqvEgxqiv5ppnCpLIk3AjiiIP1Byjx2LjZ2QSjYFCOnlpsJjHGvJ4ImLu2J1ISEwDNnMfJ1//\nOYLNRkb8tSjkKkprdpH7+i/IvOupYTXqppKOskMM9bZy5fJH8fFylS8x+cRgs5mp68hn7r2v4LRb\n0Xj5jygmrP0dAHgbhs/vs50uS387npwt8pQaT9JufYzjL/43cRHLmZ3sKuvh7RWOwSOIj3b+kvbS\nHPyTl59z/r6x2XgGxrHj0FMkRa1xFWWuz6G5vYjUGx6a0GtRd2g9gkPkyhWPolG7StbERy7nwx2/\npOnkZsKybyDjtsep3vsqRQXbcdgtGMMzyLjmxxMuWDwSrpqEZrLT70aldCWMGA2hpMdfzcGTL2Ed\n7KGvqZTyLc8w1NsKgMbTRMwl38Mvbr67H9HpwGE1016yH6fdSnToQg7k/pu+gVY89QFEBc+noHwj\nNnPfeZXmCUxdReOxj9m077fER6xApdRSVruHIVs/SV/R8iyBaatpPL6BT/f9jrjwZafXvBer3SzV\nhbzASCJPwk2+uYsySy8/ZRZJguuoIxMT/aKNl/qKabGaCVBpp3mWX0/WffcNePY2KfliFJpOfoq1\nv4trVz2Bh84PgNiIZXy485fUHVpP4hU/viDjDrTXoNEY3QLvM0IC0l2FmBUq1J6+o7bX+UUgCDLq\nW06Q7HGp+3pd8wlAQG+KGrWtSm/E6bASZEoZdt1oCEGr9Wago27M+QsyOWnrHqVq54ucKtyI027B\nwz+alBt+jW9s1pjtP09P9Ukigua6BR6Apz6AIFMKXTW5hGXfgFJrIG7NfcStuQ9RFKfU2cNu6UeQ\nydGqh4eX6HW+gEhfYwkF7/+OIL8kUtO+hQDkl2+g8IPHyLzzT3j4R1Nz4G0aj32CzdyDXKlBEGTs\nPPwXvDyCMPnG0dpRSnXDIUDEYTWfl8iTq7Rk3PYE1ftep7hoJ06HFe+o2cQvvuOCfRmZbtxxjvte\no7RoD06HDe+o2SQuvh2db9h0T+8rjSTyJNy0WIcAiMIw7HoUBkSg1S6JvOnkLwuDWP6RFL8yEj31\nBQT6JbkFHoBSoSEiaC7lZfs59e7DaLwCCJ512YTsx8ZCbTAxZOmhf7B92NjtXVUoVDrkY/y/qD18\nCEhbzfH8/2CzmQk0JdPaUUpe6YeYEpec0/5LodKh1HjS3lVBZMgZO7j+wXbM5m40XgHjWoNS40n8\nZf9N3Nr7cDrs513OQqbSMGTtO+u6xdqPfIS5fCbwRNFJR/kh2or2ugRPZCYBqavGldwgiiI9dfm0\nFu3G0tuG6HRQWZdDbMRS9+8raveh9vCjvTQHrcaLFdk/5v+3d9/RcVV3Ase/d4pGvRer2ZJVbMm9\n4YYLNoYYMLChLbCELNkAqYQsAZKTDotTgF2yCaQQvKElEAgl4IZBBjdcZclFLrJkSVZvVu9z948Z\ny5JtVc9oNKPf55w51jy9+97vXV2PfnrvFqPB9qsvIjSZdz95nOJ972G0+FGauZ5JCSuJDEsl78wu\nissPkhi7gCvn2AagWLWVbfuep7DsAOYLJldurimm8tg2rJ22pC0obkqfSayXXzCp136j1yAeT+fl\nG9Sd4IuR41ZJnlIqBPgtcANgBd4GHtJaN/VTZh1w7wWbN2qtr3NaoG4qwds2iehhapjH+U7Fh6nB\nhCLOy3FzZgnhSCZvf5oqSy+6O9TUXE1XRwu+jR1UF++gNHM9k9c8SmTakn6ONngRk64kP+Mltu1/\ngQXT7yXAL4r84s/JydtMzNw1g1rQPmXV1zGaLRzK+pCs4+9gMJqJmrqSpJX391tOGYxEz7qOnN1v\n4e8bSULcfBqbKth96BXMPoFETh7aNSqDEeNlDFaJTF9GXsY6iiuyiY2cbpsPrmg7VbWnSF926Udy\nWmuOf/gs5UcyCA1OwGzy4eTJFyjN3MiMu9Zisn8m9SUv48+c2fsO/n6RWOzLze3KWkdV7SnCQiZS\nWLqf4vKDpK5+iLKsTUSHpXUneAAGg4no8HRKyvNpriliVtptTE2xzbPoZfbjTNkBpqWu6R5hbFAG\npqWuoaBkDw2lxwkeb+tPVvj5m+R/+hfMZh+MRi8Kd71BWMoC0m/6/qDagBDO4m6t73UgClgJeAH/\nB/wB+LcBym0Avgyc+/R33LTlHiTZO5B5fuG83HSMJt3BRAI5Qg3vks/qkDhCTDJhpRidoqas4NCR\nDI7kric96QsoZaCgZC9FpfuZlX47U1Oup8vayfb9v+fkpv8lLHneRXeKtLbSerYcg8mMJSC8jzP1\nZrL4MvW2n3L0naf459Yfdm+PmLyExCWDm/fNYDKTfPWDJCz5Em0NVVgCwgY9CfGExXfR3ljNnkOv\nsOfQywB4B0Yy7fafDXgX0dFiZt1ATd4BPt71NEGBcVitnTQ0lhE1dWWfq3rUnNpL+ZEMFs9+gKR4\n20TDNXWFbNz+JIW732Lisi/3eb66M0c4s/cd5ky50/4zV1SfPc2mHU+RW7yDEwUZ+IVPIG3N94hM\nX05tfiaVxblobe1O2rTWVJ49hdHPH23tIjFuwUXn0Re+1+e22H6d1BcfI//TvzA15QZmTLoZg8FM\nQcletu1/geJ97xE//5Yh1aMQjuQ2SZ5SajJwLTBHa51p3/Yt4EOl1CNa67J+irdprStHIk5398T4\n2fziTDavNhzHCphQXBcSx3eipwxYVghXCUmYRfz8Wziw+w2OnNqAwWCkpaWWyNBU0pOuBcBoMDEr\n7VYKPt5D7emDvTrbV53YSd4nf6alzvYxEhibRsq138R/EI92A2Mmc8WDL1F7OpOO5joCYyYNq5+R\nyeKLyTK0PlkGo4lJ1z3M+EV30lB6ErNvIMHxU506fUxHcx01+ftBQ0jiLLz8bP13DSYz0277KdW5\ne6g+tQdlMJKQutg2sXMfjy0rj28nODC+O8ED26CTiXGLKMrZ1m+SV5HzGX6+EaQnnZ97LSw4gZTx\ny8gr38sV31nX6y5azOzryTr2GLuy1jE99SaUMnDoxPucrSsiee4D1Jfk0NBUgZ+PrQ9lZFgqZpMP\n2SfeY8mcr2GwP649dOKfePkEERhjGw1ddvhj/HwjmJV2a3fymBB7BUVlByg/9JEkecKl3CbJAxYC\ntecSPLst2P7Qmg+810/Z5UqpcqAW+AT4odZ68DOGjiEBRjP/NWEOVR2tVHS0EuPlS7BMnSJGOaUU\nE5ffR0TaMqqOb6e1roKWo1uZnX4Hhh6P584te6W7Oru3nS3M5si7a4mNnMbktLtp72gh++T7ZP/1\n+8z9ygt4+Q082MVgNBGWNM/xFzZIPsHj+u2/5yjF+98nL+Ol7mXClMFEwtJ7GD//Vvt7I+GpCwe9\nHq+1q+OipcgATCbLgEuRWTva8DL7XjRZs8XLj66O1osekwbHTyXl2m9y6uM/kVvwKWBbOzh51deI\nmXU9JZnr2Xv4NZbP+xYBflE0t9RgMntTULybmroCIkNSKK85QWNzJWlrHu2enqazpR4/n7CL4gjw\njaC4JmdQ9TBYWmvqi3OoPL4dbe2yr/wx12ETVgvP405J3jigoucGrXWXUqrG/r2+bMDWdy8fSALW\nAuuVUgv1+fvu4gLhZm/CL2NWd+F4O6c9wwwZYduvgKgkAqKSsHZ1cPZ0Jjn5m4kITer+JZiTtwmD\n0Uzw+GndZYo+f4vQoAmsmP9w937REem8/dF3Kc3aKLPx2/wMHN4AAB55SURBVJ0tPETulj8wKfFq\nZky62XYn7OQ/Obp1HX7hE4aV5IYmzuZ4zmdU1uQSEZoMQGtbPflndhGS2v/o3uCEmRw79BGVNSeJ\nsM/x19HZSm7RDoL7WMYsZuZqItOWUnv6IGhNSMLM7n5/aTc/zqE3fsQ7Wx7FxyeYlpZaLP7hTF7z\nKDWn9lBZW4pfwhRS5qwhMDq1+5iBsWnknXyJhqZKAvxsEy53dXVwunQvgXFpQ66Tvmityd3ye0oO\nfICvTxhGo5mSAx8QmjiHKV/80YBzIoqxyeVJnlJqLfBYP7toYNj/U7TWb/Z4e0QpdQg4BSwHMoZ7\nXCFcYe27L3Od4duuDmPUMxjNTLzqPzj24TN82FRBTMQUKmtPUV6VQ+LSe3uNjGwsz2Ny/LJed0O8\nLYFEhCbTWJHnivBHpZLM9QQFxnHFtHu6H4/OSf9XyquPU5L54bCSvMi05ZQe3MimnWtJjFmAl9mH\n/OLdWI0MmFxHTFpMyb732bzzVyTFL8bbK4C84l20tDcwefFdfZYzWfyImLT4ou3+EQlc8cCfqDy2\nnZbaUnzDYglPXYzRbCEqfVmfxxs3fRXF+95j444nSUu8Bi+zLycKttLYXMnMhZc/yfM5NXn7KDnw\nAfOm/RuTE68GFMXlWWTseY7iAx8Mer1iMba4PMkDngbWDbBPHlAG9FpHRillBELt3xsUrXW+UqoK\nSGaAJO83pUfxu+CW/6qgGFYFxw72dEIIF4maugIv/1DO7H2H3PI9eAdFkX7T94mYfGWv/bz8Q6mt\n7z2nnNXaydmGEsLGJ49kyKNae0MlYYHje/WvU0oRFjSBkvrTwzqmwWRm+h1PUrT3Hcpy7NOPpC1m\n/ILb8A7qf9kwg9HMtDuepGj33yk88ildHa0ET5jO5EV34hcxod+yfTGavRk37eohlTFZ/Jhx96/I\ny/gzmcfeQlu7CIqbwvTr/4uAcZc/yfM5FUcyCAkaz+TEVd0/g7hxMxkfM5eKI59IkudmKo5upSLn\n017bOlsdv6ydy5M8rXU1UD3QfkqpXUCwUmpWj355K7ENcdo92PMppeKAMKB0oH2/HZ0u67YK4cZC\nEmYSkjCz332iZ63m5KbfcvTURlITVtDR2cqBI2/Q2lbPuBnXjlCko59fZCIlOTvp7GrHZLT10+2y\ndlJceRj/hOGvPWv08iFh8V0k9HP3rS8miy+JS+8lcemFs2SNLO/ACNJvehxrVydoKwYn9GPubG++\n5PqvvpYgKhoLHH4+4VyR6csvWpGmoSyXA395yKHncXmSN1ha62NKqU3An5RSX8M2hcr/An/tObJW\nKXUMeExr/Z5Syg/4CbY+eWXY7t79EjgBbBrpaxBCjD7RM66lqfI0+w68zv4jf0NrKwajF5NWf3tQ\no2vdjdaajuY6DCbzoKdqAYidcyNl2VvYsuvXTE25AaUUR3I30NxSy6R5chcJcOqceMHx0zj92cv2\n5dVsdznbO5o5XbqXoBTXDfoRo5vbJHl2d2GbDHkLtsmQ3wIuTHtTgHO337qA6cCXgGCgBFty92Ot\ndf9Dt4QQY4JSBlJWfY24uTdRezoTg8mLsOQFw1qyarSrydtP3taXaKo8DShCE2eTvOpBfEJiBizr\nGxbPtNt/xslNz/PJ588A4BMcw9RbfkRAVJJzAxdEz7iW0sz1rN/2c1InLMdktHCy8DM6rO3E20c3\nC3EhJQNML6aUmg3sfynpSnlcK0Yd74wv8t2nnT9dhvAsdWeOkvX6Y0SGTSI14So6Opo5nLueDtXF\nnK/8DrP34JJarTUtNcWAxic0VqbvGEFtjTWc3vYKVce2Y7V2EjpxLglL7vHYNW/Hmh6Pa+dorQ84\n4pjudidPiDGv9ap/yFQqYsgKd71JcGAcqxY+isE+WXJs1Eze2fIIZdlb+u24r7WmraEKg9GEl18I\nvmFxIxW26MHiH8qk1Q8xabVj+20JzyVJnhBCjAENpSdIn7CyO8ED8PMJJTwkmYbSE32Wqz61l7xP\n/kxzjW0EclDcVFKu+fqwR7AKIUaOJHlCCDEKNVUVUnE0g862ZoLjpxGWsuCyOvZ7+QZT11DSa5vV\n2kVDczmhfqmXLFN35ihH3v45UeFpXHHFQ3R0tnL45Adk/fVx5n7l+e4lzUZCU1UhTZWnsQSEExib\n1udSaedoaxfVuXtoKMvFyy+YyLSlveZHFGIskCRPCCFGmaI975CX8SJeXv5YLAGUHPiAgOhJTL/j\niSGNiO1p3IxrOJXxIjEFU5kYv5jOrjYyc96ipaW2z7nhina/RVBALFcveOT8I97Iabz90X9SenAj\nExbfOexrHKzOtmaOffA01bnnZ8ryi0hgyhd/iE9w9CXLtDfXcehvP6SxMg8fnxBa2+rJ27qO9Ju/\n79Ll54QYaZLkCSHEKNJUWUBexoukJ61mVvptGA0mKmpOsmXX05ze/hrJK+8f1nFj56yhoSyXnQdf\nZM/hV7FaO9FoUq75Ov59jI5tLMslNXpRr0e83pZAIkNTaCg/Naw4hir3o+epO53NlbMfJDZqBjV1\nBezKWsfhv/+Muf/x/CUHfuR+9AId9VWsXvITIkKTaG1rYEfmn8h57xcs+MbLw06UhXA3MixKCDf0\n3KJL38EQ7q/86FYsloDuBA8gMjSFSQlXUZ79EXVnjqKtXUM+rjIYSVvzCLO//Bvir7yTiSu+wvwH\n1xEz6/o+y3j5hVDbUNxrm1VbqWsqxeLv/Ee1Hc11VOR8xqzJX2Ri/CIsXn5ER6Rz5ez7aa4p4mxB\n1kVlOtuaqTqxk2kpNxARaktevS0BLJx5H12dbVQe3+H0uIUYLSTJE8IN7Zz2DFt/4ePqMIQTdLU3\nY/EK6E7wzvHxDqazvZmDr32P3b+/j5r8zD6O0L+AqCTGz7+V2Dk3YgkI63ff6JmrOVN2gJxTm+jq\n6qCtvYk92S/T3FzNuOnXDOv8Q9HWWIO2dhEe0vtO47n3rXUVF5Xpam9GW7sI8O29LJqPJRCT0UJn\nS4PzAhZilJEkTwghRpGguKnUN5RQWZPbvc1q7eRU0XbCQ5JYveTHhHhHceTtn9NSO+DqjJdl3Ixr\niJl1PXsPv8Zf1z/Amxu/wcnCz0i55hsOXZe1L95BkRiMXpRWHum1vbTyMGCboPlCXn4heAdEkF/8\nea/tZ8qz6OxsJSBmkvMCFmKUkT55QggxioSnLiRgXApbPv81qRNW4OsdzKmi7dTVF7Nq8eNEhCaz\n4orv8NZHD1OatYGJy+9zWixKGUi55uvEzrmRmvwDGExmwlMWOH1UbXtTLdbODiyBEUTPuJbsg+9h\nMJiIi5pBdV0B+4++QWD0JAJj0y6O2WBk/JV3cWLDc1itnYyPmUtdQwk5eZsJmTCToLgpTo3dmZqq\nCina/Tb1Z45i9gkgcuoKYmauRvXoMylET5LkCSHEKGIwmph+x5Oc3v4qxw9n0NnWiL9vBKsWP05U\nmO0ulMlkITx4Is01JQMczTF8w+JGZALkpqpCcjf+lrPFtjt3fsExTLjqy1itnWRmv82Bo28AEJo4\nh0nXf7fPaVSip1+DMhgp3PE3Cvf/HqPZm6jpVzNx2b8POPXKaNVQlkvW649hMfuRMG4Ojc3V5G75\nPfVnjjJ5zffc9rqEc0mSJ4QQo4zJ25/kqx8k+eoHyXz1UXzarESGnp/LrrOrnaqzeUQmOL9f3Ehp\nb64j+7VHCW3T/Adp+GDik7MlHH1nLdPv+gUJS+6hpeYMXv5h+AQPvKzfuKkriZqygq72Foxmi9vf\n7crf+n8E+ESwesmPMZssAOQV7WD7gT8QM/sGguLSXRyhGI2kT54QQoxi8VfcTHlVDnsOvUJ9YxnV\nZ/PZuuc3dHa1ETNztavDc5iyrE3otmYe0zNZpKKZpSJ4mOnEqgCKdr2Jl28QQXFTBpXgnaOUwmTx\ndesEr7OtmaI9b1NbkIlCcaY8E6u2ApAYtxBv7yBq8va6OEoxWsmdPCHc1M5pz5Dx0nSuevtKV4ci\nnCg8dRFJK75K7rZXOJ6/BQAvv1DS/+WH+ITEuDi6gdWXHKfw8zdpKDmOl28QUdNWETtnzUWJV0NZ\nLskEEaS8urcZlGK2DmVTWe6Fhx0TOloayHrtUZpri4mJmEZ7ZzPb9j1PUex+lsz5GlpbsVo7UQb5\nVS4uTVqGEG6s5e8HwCBJnqeLm3cz46ZfQ33xUZTRTFDclEEvcdZUVUjB9tc5W3AQg8mLiLSlTFj0\nr5i8/Z0cNZwtzCb7jR8R6BfFpNgl1DeVcyrjRRpKT5B246O99rUEhFKiWuiyWjH2mOD4DE14+Yc6\nPdbRqHDXG7TXV7Fm+ZMEB8QCcLp4N5/t+x0JsQuoayihvb2J8EmLXRypGK0kyRNCCDdgsvgSOnHu\nkMo0VRVy8JX/xNvsT/qEq2nvaCL34EbOFmQx655nMJi8Bj7IZcjLWEd4cCLXLP5+97x/pwpnsCPz\nj8TOu5nA6PP9DMdNv4b9+z/gZY5zm07GgoFPKeEAlaTMut2pcY5WVcd3MjFuUXeCB5AQO5/s4++y\nI/NPdHQ0E7/gdvwjElwXpBjVJMkTQggPVbjzb1hMfqxZ9gRms23y7InxV/Lhpz+h/OhWop04oXFn\nayMNZSeYPuv+XhM7J8YvYs/hV6nNP9AryfOPnEjqF77Fjs3Ps91ahgFFF1aiZ64meuYXnBbnaKat\nnRiN5ou2G41eGHz8mfYv3yc0cbYLIhPuQpI8IYTwUGcLspgUt7Q7wQMIC04gPCSJswVZTk3ylMEE\nykBHZ0uv7V1dHVitnRgukbxEz7iWsJQFVJ/8HGtnGyEJsy454fFYEZo0j7xjO5mStBof72AAyquP\nU302n8k3PCIJnhiQJHlCuLGDG0w8xfO88Ye7yHo/2NXhiFHGYLLQ1t7Ya5vWmraORvzNFqee2+jl\nTVjSPI6c2kh89Gz8fMKwaitZx/5BV1cHYamLqC85RltDNf6Rid2DSLx8g4ieca1TY3MX4xfdQXXu\nHt7L+AEJMVfQ3tFMYek+guKmEjFZ+uKKgUmSJ4QHuDe1le+6Oggx6kROWcapPe+RFL+YiNAUtNbk\n5G2iobGMiekPOf38SSu/StZrj/POlu8RGZpKQ3MFTc1VxC+8nZx3n6KxIq9734hJVzLp+ocxmr2d\nHpe78A6MZPaX/puiPf/gTP4BDCYvJiy9h9jZay55J1SIC0mSJ4QQHip+/m2cPZ3Fhm1PEBqcQFtH\nE01NlcTOuZGg+GmXLNNcXUR17h4AwpLnX9ZKFz7B0cy577eUZW+mvvQ4gXEJpExdwYn1z2Foa+fq\nhY8SEjSeM2WZ7Dn8Kqe2/JHU1d8e9vk8kSUwnOSr73d1GMJNSZInhBBuQGtNV3sLBpPXoKdPMVl8\nmXH3L6k8voPa/Ey8zRaS05YSFDflomWwtNbkf7qOot1vYzJZ0Brytr5E/PxbSVz25WEvm2X2CSB+\n/i3d72vyD9BcU8TqJT8mIjQZgJQJy2hrbyTz8NtMvOq+EZneRYixQJI8IYQY5SqObqVgx19prjmD\n0exN5JQVTFz+75gsvgOWNRjNRKUvJyp9eb/7VZ/cRdHut5mdfgdpE20DMo6e2kjm7r8TGDuZ8JSF\njrgUWs+WAorwkKRe2yNCk9HWTtoaqiXJE8JBZFkzIYQYxcqPZJDzz18T6hXJlXMeZMrEL1B1JIPD\nb/0UrbXDzlOatZmI0BSmplyP0WjGaDQzLXUN4SHJlGVtdth5bKNlNeXVx3ptL6vKwWDywhIY4bBz\nCTHWSZInhAdoveofPPtImavDEA6mtaZg+2uMj57LVVc8xMS4Rcyc/EWWzv0GdWeOcLYgy2Hn6miu\nI9Dv4nVhg/zH0dFc57DzBMVPwz8yiW0Hfk9+8efUNZRyJHcDh068T/SMLwzq7qQQYnDkca0QHmJ2\neCLQMuB+wn10tNTTcraUxJRbevWJi4mYhpeXP/UlxwhJmOmQcwXEpHLmyGd0dLR0z6vX0dHCmYps\nwqcuc8g5AJRSTL31Jxz74Gm27Xvets1gZNy0VUy86j6HnUcIIUmeEEKMWkazN8pgoqGpstf2tvYG\nWzLmE+Cwc8XNvYnyQx+zccd/kZZo65OXk7+ZLt1J7JybHHYeAEtAGDPuXEtzTTHtjdX4hsXj5Rfi\n0HMIIeRxrRBCjFpGs4XItKUczv2QiuoTALS2N7Arax3KaCJi8hKHncsnJIYZd63FGhDIzoMvsvPg\ni1gDA5l+51p8QqIddp6efENjCR4/XRI8IZxE7uQJIcQolrTyqxyqKmTj9ifx8Qmhra0BlIG0mx7F\n7BPo0HMFjEth5t2/pLPVtkqGjHIVwr1JkieEh9B7PwJkqSNPY/YJZNaXnqU6bx8NJScw+wYRmb4M\nL98gp51TkjshPIMkeUJ4iF33ZfNsRjLfffriEZLuoLO1karc3XS1txA8fjp+4eNdHdKooQxGwpPn\nE54839WhCCHciCR5QniQyb96EwzutyxU5bHtHF//33R1tKKUEa27iJq6kkmrH0IZjK4OTwgh3JIk\neUIIl2o5W0bOP3/N+HGzmTf1biyWAE4VbmN39l/wDR/P+Pm3ujpEIYRwSzK6VgjhUuWHtmAyerF4\n1lfx9QnBaDCRmnAVE+MWU5a5wdXhCSGE25IkTwjhUm2N1QT4RWEyWXptDwmKp62xxkVRCSGE+5Mk\nTwjhUv5RSdTWFdLYXNW9TWtNUVkm/lETXRiZEEK4N0nyhPAgBzeY2PoLH1eHMSRRU1bg5RfC5p2/\n5FThdoorsvl0328pr8ohfuHtrg5PCCHcliR5QniYndOe4dlHylwdxqCZLL5Mv/MpTGFR7Mj8Ix/v\nepqKxgIm3/CITBkihBCXQUbXCiFczjc0lhl3rqW9sYaujla8g6Jk6hQhhLhMkuQJIUYNL/9QV4cg\nhBAeQx7XCiGEEEJ4IEnyhBBCCCE8kCR5QnigWfm5rg5BCCGEi0mSJ4QH2nVfNhm3bHd1GEIIIVxI\nkjwhhBBCCA8kSZ4QQgghhAeSJE8IIYQQwgNJkieEh9p1XzYzbjzr6jCEEEK4iCR5QniwOx54XRI9\nIYQYoyTJE0IIIYTwQJLkCSGEEEJ4IEnyhBBCCCE8kFsleUqpHyildiilmpRSNUMo93OlVIlSqlkp\n9ZFSKtmZcXqij84WuzqEUUXqo7eKo1tdHcKoIvVxntRFb1IfvUl9OJdbJXmAGXgTeGGwBZRSjwHf\nBO4HrgCagE1KKS+nROihPqorcXUIo4rUR28VOZ+6OoRRRerjPKmL3qQ+epP6cC63SvK01j/TWj8H\nHBpCsYeAJ7TWH2itDwNfAmKAm50RoxCjzR0PvM6zj5S5OgwhhBAjzK2SvKFSSiUC44CPz23TWtcD\nu4GFropLCCGEEMLZPDrJw5bgaaD8gu3l9u8JIYQQQngkk6sDUEqtBR7rZxcNpGmtT4xQSADeAAVt\njSN4ytGtqauT4y11rg5j1HC3+vDKKqChzHntubO1mYayXKcd391IfZwnddGb1EdvUh/nNVcXnfvS\n21HHVFprRx1reAEoFQaEDbBbnta6s0eZe4H/1lqHDnDsROAUMFNrnd1j+1YgU2v9cB/l7gJeG9wV\nCCGEEEI4zN1a69cdcSCX38nTWlcD1U46dr5SqgxYCWQDKKUCgfnA7/opugm4GzgNtDojNiGEEEKI\nHryBBGw5iEO4PMkbCqVUPBAKTACMSqkZ9m/laq2b7PscAx7TWr9n/97/AD9USuViS9qeAM4A79EH\ne+LpkCxaCCGEEGKQdjryYG6V5AE/xzYFyjkH7P9eBXxm/zoFCDq3g9b6V0opX+APQDCwDVittW53\nfrhCCCGEEK7h8j55QgghhBDC8Tx9ChUhhBBCiDFJkjwhhBBCCA8kSZ6dUuoHSqkdSqkmpVTNIMus\nU0pZL3itd3asI2E49WEv93OlVIlSqlkp9ZFSKtmZcY4EpVSIUuo1pVSdUqpWKfWiUspvgDIe1TaU\nUt9QSuUrpVqUUp8rpeYNsP9ypdR+pVSrUuqEfdojjzCUulBKLbtEO+hSSkWOZMzOopRaopR6XylV\nbL+2GwdRxpPbxpDqw5Pbh1Lq+0qpPUqpeqVUuVLqHaVU6iDKeWT7GE59OKJ9SJJ3nhl4E3hhiOU2\nAFHYVtAYB9zp4LhcZcj1oZR6DPgmcD9wBdAEbFJKeTklwpHzOpCGbSqe64Gl2AbyDMQj2oZS6g7g\nGeAnwCwgC9vPNbyP/ROAD7AtJzgDeA54USm1aiTidaah1oWdxjYg7Fw7iNZaVzg71hHiBxwEvo7t\nOvvlyW3Dbkj1Yeep7WMJ8L/Ypiy7GtvvlM1KKZ++Cnh4+xhyfdhdXvvQWsurxwu4F6gZ5L7rgH+4\nOuZRVB8lwMM93gcCLcDtrr6Oy7j+yYAVmNVj27VAJzBuLLQN4HPguR7vFbZpiB7tY/9fAtkXbPsr\nsN7V1+KCulgGdAGBro59BOrGCtw4wD4e2zaGWR9jqX2E2+vkSmkfg66Py24fcifv8i2333o9ppR6\nXinV7yocnsq+usg4bH+BAaC1rgd2AwtdFZcDLARqtdaZPbZtwfbX1fwByrp921BKmYE59P65amx1\n0NfPdYH9+z1t6md/tzDMugBbInjQ3o1hs1JqkXMjHdU8sm1cprHSPoKxfW721/1nLLWPwdQHXGb7\nkCTv8mzANm/fCuBRbFn3eqWUcmlUrjEOW4Mtv2B7uf177moc0OvWuNa6C9t/zP6uy1PaRjhgZGg/\n13F97B+olLI4NrwRNZy6KAUeAG4BvggUAVuVUjOdFeQo56ltY7jGRPuwf+79D7Bda320n13HRPsY\nQn1cdvtwt8mQh0QptRZ4rJ9dNJCmtT4xnONrrd/s8faIUuoQtrVylwMZwzmmMzm7PtzJYOtiuMd3\nt7YhnMP+f6nn/6fPlVJJwMPYukKIMWwMtY/ngXRgsasDGSUGVR+OaB8eneQBT2PrG9WfPEedTNvW\nyq0Ckhmdv8idWR9l2G4rR9H7L7EoIPOSJVxrsHVRBvQayaSUMmJbXq9ssCdzg7bRlypsfUKiLtge\nRd/XX9bH/vVa6zbHhjeihlMXl7KHsfvLzlPbhiN5VPtQSv0WuA5YorUuHWB3j28fQ6yPSxlS+/Do\nJE/b1qCtHqnzKaXigDBst1hHHWfWhz2JKcM2AjUbQCkViK3f2u+ccc7LMdi6UErtAoKVUrN69Mtb\niS2h3T3Y8432ttEXrXWHUmo/tmt+H7ofNawEftNHsV3A6gu2XWPf7raGWReXMhM3awcO5JFtw8E8\npn3YE5qbgGVa68JBFPHo9jGM+riUobUPV48wGS0vIB7bkO0fA3X2r2cAfj32OQbcZP/aD/gVtiRm\nArYP+n1ADmB29fWMdH3Y3z+KLXFaA0wD3gVOAl6uvp7LrIv19p/tPGx/QR0HXrlgH49tG8DtQDO2\nPoaTsU0fUw1E2L+/FvhLj/0TgAZsI+UmYZtOoh242tXX4oK6eAi4EUgCpmDrh9MBLHf1tTioPvzs\nnwszsY0U/I79ffxYaxvDrA+PbR/YHknWYps6JKrHy7vHPk+NlfYxzPq47Pbh8gsfLS9sj+66LvFa\n2mOfLuBL9q+9gY3Ybi+3Ynu098K5D3t3fw21Pnps+ym2qVSasY2KSnb1tTigLoKBV7Elu7XAnwDf\nC/bx6LZh/7A9jW1KnF3A3AvayicX7L8U2G/f/yRwj6uvwRV1AXzPfv1NQCW2kblLRzpmJ9bFMmzJ\nzIWfEy+N0bYxpPrw5PbRRz30+p0xltrHcOrDEe1D2Q8khBBCCCE8iEyhIoQQQgjhgSTJE0IIIYTw\nQJLkCSGEEEJ4IEnyhBBCCCE8kCR5QgghhBAeSJI8IYQQQggPJEmeEEIIIYQHkiRPCCGEEMIDSZIn\nhBBCCOGBJMkTQggHUkqNU0q9ppQ6rpTqUko96+qYhBBjkyR5QgjhWBagAngCOOjiWIQQY5gkeUII\nMQRKqXClVKlS6vEe2xYppdqUUldprQu01g9rrV8F6l0YqhBijDO5OgAhhHAnWusqpdR9wLtKqc3A\nCeBl4Dda6wzXRieEEOdJkieEEEOktd6glPoj8DqwD2gEfuDaqIQQojd5XCuEEMPzPWx/KN8K3KW1\n7nBxPEII0YskeUIIMTzJQAy2z9FEF8cihBAXkce1QggxREopM/AK8DfgOPBnpdRUrXWVayMTQojz\nJMkTQoihewoIBL4FNAPXAeuANQBKqRmAAvyBCPv7dq11jmvCFUKMRUpr7eoYhBDCbSillgGbgeVa\n6132bROwzYn3uNb6D0opK3Dhh2uB1nriyEYrhBjLJMkTQgghhPBAMvBCCCGEEMIDSZInhBBCCOGB\nJMkTQgghhPBAkuQJIYQQQnggSfKEEEIIITyQJHlCCCGEEB5IkjwhhBBCCA8kSZ4QQgghhAeSJE8I\nIYQQwgNJkieEEEII4YEkyRNCCCGE8ECS5AkhhBBCeKD/B7zjtwYAXWpnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12dcc470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train 3-layer model\n",
    "layers_dims = [train_X.shape[0], 5, 2, 1]\n",
    "parameters = model(train_X, train_Y, layers_dims, optimizer = \"adam\")\n",
    "\n",
    "# Predict\n",
    "predictions = predict(train_X, train_Y, parameters)\n",
    "\n",
    "# Plot decision boundary\n",
    "plt.title(\"Model with Adam optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,2.5])\n",
    "axes.set_ylim([-1,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
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    "### 5.4 - Summary\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td>\n",
    "        **optimization method**\n",
    "        </td>\n",
    "        <td>\n",
    "        **accuracy**\n",
    "        </td>\n",
    "        <td>\n",
    "        **cost shape**\n",
    "        </td>\n",
    "\n",
    "    </tr>\n",
    "        <td>\n",
    "        Gradient descent\n",
    "        </td>\n",
    "        <td>\n",
    "        79.7%\n",
    "        </td>\n",
    "        <td>\n",
    "        oscillations\n",
    "        </td>\n",
    "    <tr>\n",
    "        <td>\n",
    "        Momentum\n",
    "        </td>\n",
    "        <td>\n",
    "        79.7%\n",
    "        </td>\n",
    "        <td>\n",
    "        oscillations\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "        Adam\n",
    "        </td>\n",
    "        <td>\n",
    "        94%\n",
    "        </td>\n",
    "        <td>\n",
    "        smoother\n",
    "        </td>\n",
    "    </tr>\n",
    "</table> \n",
    "\n",
    "Momentum usually helps, but given the small learning rate and the simplistic dataset, its impact is almost negligeable. Also, the huge oscillations you see in the cost come from the fact that some minibatches are more difficult thans others for the optimization algorithm.\n",
    "\n",
    "Adam on the other hand, clearly outperforms mini-batch gradient descent and Momentum. If you run the model for more epochs on this simple dataset, all three methods will lead to very good results. However, you've seen that Adam converges a lot faster.\n",
    "\n",
    "Some advantages of Adam include:\n",
    "- Relatively low memory requirements (though higher than gradient descent and gradient descent with momentum) \n",
    "- Usually works well even with little tuning of hyperparameters (except $\\alpha$)"
   ]
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   "source": [
    "各种优化算法表现：\n",
    "\n",
    "<img src=\"./images/opt1.gif\">\n",
    "<img src=\"./images/opt2.gif\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**References**:\n",
    "\n",
    "- Adam paper: https://arxiv.org/pdf/1412.6980.pdf"
   ]
  }
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